EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish on a monthly frequency (12 issues per year). INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO, Crossref, Dimensions eISSN: 2032-9407
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A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
INTRODUCTION: Roadside perception systems in intelligent transportation have stringent requirements for all-weather, high-precision, and real-time vehicle target tracking. However, there are inherent technical bottlenecks in such systems, including sparse millimeter-wave radar point clouds, severe noise interference, and the poor adaptability of traditional algorithms to distance-dependent density variations. OBJECTIVES: This study aims to propose a robust vehicle target tracking method based on roadside millimeter-wave radar to address the aforementioned technical bottlenecks and meet the stringent requirements of roadside perception systems for vehicle target tracking. METHODS: The research employs three key technical methods: Firstly, a point cloud distribution histogram is constructed to accurately localize the road area, and radar cross-section (RCS) characteristics are synergistically integrated with motion attributes to eliminate static clutter, multipath false plots, and electromagnetic noise. Secondly, a Distance-Zoned Multi-Frame DBSCAN (DZM-DBSCAN) clustering algorithm is proposed, which partitions the radar’s effective detection range into near-distance and far-distance intervals based on the physical principle of point cloud density attenuation with increasing detection distance, dynamically optimizes clustering parameters (Eps, MinPts) for each interval using local density statistics from k-nearest neighbor (KNN) analysis, and suppresses single-frame isolated false clusters through inter-frame motion continuity constraints. Finally, a multi-target tracking framework is established by combining a dynamically threshold-adjusted Kalman Filter (KF) with multi-feature weighted Hungarian matching. RESULTS: The main results obtained in this paper are the following: Experimental validation on the public RadarData dataset shows that the DZM-DBSCAN algorithm achieves a Silhouette Coefficient (SC) of 0.8456, representing a 10.09% improvement over the traditional DBSCAN (0.7681), and a Davies-Bouldin Index (DBI) of 0.1936, which is 10.66% lower than that of the traditional DBSCAN (0.2167). This effectively resolves the long-distance target missing detection issue prevalent in conventional methods. Additionally, the proposed tracking method achieves an optimal balance between tracking accuracy and continuity, with an average single-frame processing time of only 1.08 ms, fully meeting the real-time requirements of roadside perception systems. CONCLUSION: The proposed robust vehicle target tracking method based on roadside millimeter-wave radar provides a reliable all-weather, high-precision, and real-time roadside vehicle perception solution for intelligent transportation systems. Thus, this work holds significant engineering application potential and theoretical value.
Copyright (c) 2026 Hongcai Chen, Yu Cheng, Yaheng Ren, Yaoxing Kang https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Based Optimization Method for Coverage of IoT Upload Link Communication
To address the degradation in uplink communication quality of the Internet of Things (IoT) caused by long-distance signal attenuation, multipath interference, and dynamic environmental changes, this paper proposes an artificial intelligence (AI)-based optimization method for IoT uplink communication coverage. First, service sampling points, base stations, and antenna parameters are defined within a target area, and a signal propagation model is constructed. The Sigmoid function is then used to smoothly evaluate service quality, and the coverage optimization problem is formulated as an objective function that maximizes coverage. The Q-learning algorithm is employed to achieve dynamic optimization by designing the state space (e.g., coverage rate, number of devices), action space (e.g., antenna parameter adjustments), and reward function (e.g., coverage improvement). Through iterative Q-value updates, the base station controller adaptively adjusts antenna configurations to balance coverage and interference, thereby improving long-term communication performance. To further enhance optimization efficiency, prioritized experience replay and an adaptive exploration mechanism are introduced to dynamically adjust the exploration–exploitation trade-off during learning, accelerating convergence and improving adaptability in dynamic IoT environments. Experimental results show that the proposed method significantly improves the communication performance of the uplink: the number of complete sensor data upload packets increases by 20%–25%, the number of transmission interruptions decreases by 70%–80%, and the average communication duration is reduced by 30%–40%, verifying the effectiveness and robustness of the method.
Data Security and Privacy Protection in New Distributed Networks and System
Copyright (c) 2026 Sheng Ye https://creativecommons.org/licenses/by-nc-sa/4.0
Energy-Efficient Lightweight Edge Inference via MOSI-AirComp: Over-the-Air Convolution and Communication-Aware Dual-Branch Training
Lightweight, energy-efficient edge intelligence underpins next-generation pattern recognition for IoT and wireless edge computing. Over-the-air computing (AirComp) is a promising communication-computation integration paradigm, yet its distributed inference deployment is severely hindered by signal phase misalignment and channel-induced performance degradation. This paper proposes a lightweight energy-efficient edge inference framework based on the novel Multiple-Output Single-Input AirComp (MOSI-AirComp) architecture, which inherently eliminates the phase alignment issue of traditional AirComp systems. A communication-aware dual-branch training strategy is introduced to boost robustness against wireless channel impairments without compromising inference efficiency, incorporating channel fading and noise in training while keeping inference model complexity unchanged for adaptive recognition in dynamic edge environments. Additionally, a weight-aware power control scheme enables over-the-air convolution, executing multiply–accumulate operations via wireless signal superposition. An improved TSP-based node selection and resource scheduling algorithm, considering model weights and path loss, achieves a desirable energy-accuracy trade-off for collaborative edge inference. Extensive simulations on MNIST/CIFAR-10 with LeNet-5/VGGNet-16 show the framework significantly improves inference accuracy and MSE performance under various SNRs and power constraints, while reducing edge device latency and computational load, providing an effective solution for lightweight energy-efficient pattern recognition in edge intelligence systems. The proposed design also provides quantization-friendly lightweight benefits: CNN weights and intermediate features can be mapped to bounded antenna-level power-control factors and low-bit transmitted amplitudes, thereby reducing high-precision multiply-accumulate operations, memory access, latency, and energy consumption on resource-constrained edge devices.
Copyright (c) 2026 YUSHUAI ZHAO https://creativecommons.org/licenses/by-nc-sa/4.0
Generative AI-based privacy protection and security management mechanism for distributed financial systems
In distributed financial systems, cross-institutional data sharing faces significant security challenges, including sensitive data leakage risks, unauthorized access, and inadequate privacy protection mechanisms. To address these issues, this paper proposes a generative AI-based privacy protection and security management mechanism for distributed financial systems. The proposed method employs a Gradient Penalty Generative Adversarial Network (GP-GAN) to generate privacy-preserving synthetic data that retains the statistical distribution characteristics of original financial data without exposing sensitive information such as account details and transaction records. A blockchain-based fine-grained access control model is constructed to allocate data access permissions according to user roles (ordinary users, financial institution administrators, regulators) and standardize access processes through smart contracts. The Probabilistic Neural Network (PNN) is restructured from risk prediction to abnormal attack detection, enabling real-time identification of side-channel attacks and data injection attacks by analyzing node data transmission characteristics. Furthermore, a game-theoretic weighting method balances multi-node security collaboration and data sharing efficiency. Experimental results demonstrate that the proposed mechanism achieves a privacy protection strength of 92.6%, an access control accuracy of 96.3%, an attack detection recall rate of 89.7%, and an edge node processing latency below 50 ms, effectively ensuring data security in distributed financial systems.
Copyright (c) 2026 Xiaochuan Tian https://creativecommons.org/licenses/by-nc-sa/4.0
Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
Data heterogeneity, the complexity of privacy budget allocation, and the imbalance between privacy and performance lead to a limited scope of privacy protection constraints and fail to ensure data integrity. Therefore, an edge computing communication privacy protection method based on federated learning algorithm is proposed. Participants use federated learning to locally train the sensing data to obtain a local model, avoiding the interaction of raw data with edge computing nodes and the perception platform. The parameter values of the trained model are perturbed by noise using the adaptive differential privacy technology and uploaded to the edge computing node. The edge computing node performs edge aggregation on the noisy model parameters and uploads them to the perception platform to complete the global aggregation operation, realizing edge computing communication privacy protection. A performance loss constraint mechanism suitable for federated learning is proposed and designed, and the performance loss of the adaptive differential privacy federated model is reduced by optimizing the constraint scope of the loss function, improving the privacy protection effect. Experiments show that this method can effectively add noise to the local model parameters and achieve privacy protection for edge computing communication; the performance loss of this method’s privacy protection is small, about 0.4; when transmitting different types of data in edge computing communication, the data integrity after privacy protection processing by this method is above 0.98, with excellent privacy protection effect.
Copyright (c) 2026 Jiamei Xue, Wengang Yan https://creativecommons.org/licenses/by-nc-sa/4.0
A Predictive and Multi-Objective Cloud–Edge Scheduling Framework with Task-Aware Encoding for Real-Time Educational Services
INTRODUCTION: With the rapid expansion of real-time interactive educational applications, including smart classrooms, online assessments, and learning behavior analytics, cloud–edge collaborative computing has become a critical infrastructure for ensuring low-latency and high-reliability service delivery. However, existing scheduling frameworks often rely on reactive or coarse-grained strategies that inadequately capture task-level latency sensitivity and system dynamics, resulting in inefficient resource utilization and unstable performance under high-concurrency educational workloads. OBJECTIVES: This study aims to design a latency-aware cloud–edge collaborative scheduling architecture capable of accommodating heterogeneous educational tasks, dynamically adapting to fluctuating resource and network conditions, and providing differentiated service prioritization to meet stringent real-time performance requirements. METHODS: The proposed architecture integrates three tightly coupled components: (1) a task feature encoder that models multimodal educational tasks in terms of computational load, data scale, and latency sensitivity; (2) a latency prediction model that leverages historical system states to anticipate future end-to-end latency trends; and (3) a multi-objective scheduling strategy that jointly optimizes latency, resource utilization, and migration cost through adaptive decision-making across cloud and edge nodes. RESULTS: Experimental evaluations conducted on real-world educational task datasets demonstrate that the proposed approach significantly outperforms mainstream reinforcement learning–based schedulers. Specifically, it reduces average end-to-end latency by 18.1%, improves average resource utilization by 8.0%, and achieves a task success rate of 96.8% under high-concurrency conditions, while maintaining lower latency jitter and migration overhead. CONCLUSION: The proposed latency-aware cloud–edge collaborative scheduling architecture provides an effective and scalable solution for guaranteeing low-latency performance in real-time educational services. By combining task-aware representation, predictive latency modeling, and multi-objective optimization, the framework offers strong practical value for deployment in smart classrooms and large-scale online education platforms.
Scheduling optimization and load balancing in scalable distributed systems
Copyright (c) 2026 Yao Sun, Zhaozhen Liang, Jingxin Liu https://creativecommons.org/licenses/by-nc-sa/4.0
Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
We, the Publisher, have retracted the following article: Fan et al. (2025). The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data. EAI Endorsed Scal Inf Syst. https://doi.org/10.4108/eetsis.7015 The article has been retracted due to authorship dispute. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 Xiaoxue Fan, Ping Zhang, Bingxin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
The rapid proliferation of rooftop photovoltaics and behind-the-meter battery storage is creating systemic risks in distribution networks. This includes local network constraint violations, insufficient outage resilience, and enlarged cybersecurity attack surfaces, leaving millions of demand-side distributed energy resources (DERs) to operate without coordinated oversight. Existing solutions (dynamic operating envelopes, virtual power plants, peer-to-peer trading, and single-household energy management systems) each address only partial aspects of this challenge. Based on multi-agent reinforcement learning, this paper proposes a Decentralised Coordination Framework (DCF) that organises the energy dispatch into a three-tier hierarchical architecture. Specifically, it has a user layer executing Proximal Policy Optimisation (PPO) for local dispatch, a feeder layer in which a dynamically elected L1 leader applies MADDPG to coordinate community flexibility via a Virtual Aggregation Unit (VAU); and a cross-feeder layer where an L2 leader manages inter-community balancing and market interfaces. It integrates a directed acyclic graph (DAG)-based verifiable execution ledger, Paillier homomorphic encryption, and LLM-based anomaly detection to enhance security. Potential market participation pathway and revenue distribution mechanism are proposed to align with the Australian National Electricity Market. The DCF provides a scalable, market-ready foundation for commercial demandside DER deployment under high renewable penetration.
Copyright (c) 2026 Bo Li, Yong-Feng Ge, Yuan Miao, Ruoxuan Cui, Xin Li, Kate Wang, Hua Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Adaptive Scheduling and Compute Demand Prediction for Animation Rendering Tasks Using Machine Learning
INTRODUCTION: The animation industry’s growing demand for high-resolution, multi-scene rendering has led to highly heterogeneous and dynamic workloads, challenging traditional scheduling systems. OBJECTIVES: This study aims to overcome the limitations of static or rule-based schedulers by developing an adaptive, learning-driven approach that dynamically matches rendering tasks to compute resources under fluctuating conditions. METHODS: We propose a machine learning-based scheduling and compute prediction model that integrates multi-dimensional task feature vectors with a lightweight sequence prediction network and a multi-objective optimization strategy. This enables real-time estimation of rendering duration, compute demand, and node load for dynamic resource allocation. The framework explicitly models temporal dependencies across frames and accounts for GPU heterogeneity through unified task representations. RESULTS: Evaluated on a cluster with 12 representative animation scene categories and 3 GPU architectures, our method shortens average task latency by 11.0%, improves node utilization by 3.9%, and reduces energy consumption by 7.8% over the best baseline, while maintaining robust performance even under severe (20%) state noise. CONCLUSION: The proposed model demonstrates strong adaptability and efficiency in complex rendering environments, offering a scalable foundation for intelligent, cross-platform scheduling in next-generation rendering infrastructures, particularly beneficial for cloud rendering, virtual production, and energy-constrained GPU clusters, while supporting real-time responsiveness and cost-effective resource management.
Copyright (c) 2026 Yi Yang, Muchen Zhang, Zhaopei Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
This paper presents a novel Numerical Index Modulation-assisted Differential Chaos Shift Keying (NIMDCSK) system, architected on a multi-carrier framework where numerous information-bearing subcarriers are supported by a single common reference subcarrier. The proposed system introduces an additional dimension for data transmission by embedding information bits into the dynamic allocation of signal energy across subcarriers. This mechanism not only boosts the transmission rate but also significantly enhances physical layer security. Specifically, the NIM-DCSK scheme obfuscates its transmission characteristics through pseudo-random energy allocation patterns, where deliberately varying power levels create a complex and non-stationary signal signature. This fluctuating energy distribution effectively masks the underlying data structure, making it extremely difficult for unauthorized eavesdroppers to perform feature analysis or achieve successful decoding. Compared to conventional DCSK systems, comprehensive analyses demonstrate that the NIM-DCSK system achieves substantial gains in both energy efficiency (EE) and spectral efficiency (SE), alongside improved bit error rate (BER) performance under various channel conditions. These advantages are realized without a commensurate increase in structural complexity. The inherent security features, including inherent resistance to passive interception and a reduced vulnerability to statistical analysis, are key benefits. Consequently, this work facilitates the integration of index modulation with chaos-based communications, ensuring high-rate transmissions coupled with robust security. The proposed system is, therefore, highly suitable for applications demanding reliable and secure wireless links, such as secure sensor networks, tactical military communications, and confidential IoT deployments.
Multi-carrier differential chaos shift keying
Copyright (c) 2026 Wang Chen, Chao Duan https://creativecommons.org/licenses/by-nc-sa/4.0
Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
INTRODUCTION: As digital transformation progresses, power communication networks become exposed to increasingly complex security threats. Moreover, traditional routing algorithms cannot perceive or respond to dynamic security threats, making reliable data transmission difficult to achieve under network attack conditions. OBJECTIVES: Therefore, this study proposes a risk-aware secure routing (RASR) mechanism for power communication networks based on graph neural networks (GNNs). METHODS: The mechanism first introduces an autoencoder-graph neural network (AGNN) architecture for determining critical nodes, thus establishing a scoring prediction model customized to the structure of power communication backbone networks. It then integrates the essential scores of the nodes, historical failure rates, and traffic load factors to devise a path node risk quantification model. Finally, it incorporates risk quantification results into routing policies to enable proactive routing avoidance and thus bypass high-risk nodes. RESULTS: Experimental results show that the RASR algorithm effectively meets the differentiated service requirements of heterogeneous traffic—including delay-sensitive, bandwidth-sensitive, and reliability-sensitive traffic. Compared with traditional routing algorithms, it demonstrates greater stability and fault tolerance under high-risk attack scenarios while eliminating the need for redundant backup strategies, thereby markedly reducing resource overhead. CONCLUSION: Therefore, the proposed mechanism offers important theoretical support and a novel technical approach for establishing secure, reliable power communication networks.
Copyright (c) 2026 Yanjun Zhao, Yue Zhang, Xiaowei Zhao, Liyu Liu, Xiang Wang, Kaiyue An https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
INTRODUCTION: Foundation models deployed in power grid infrastructure face escalating security threats from advancing quantum computing capabilities, which can compromise classical encryption protecting multi-gigabyte model parameters during distributed transmission and storage across geographically dispersed substations. OBJECTIVES: This research develops and validates a quantum-secured communication framework integrating quantum key distribution with hierarchical encryption mechanisms specifically designed for protecting foundation model parameters in operational power grid environments. METHODS: A dual-channel quantum communication architecture was deployed across five substations in Shanxi Province spanning 580 kilometers, implementing BB84 protocol with decoy state techniques for quantum key generation, dynamic key-data mapping algorithms for parameter encryption, and Ceph-based distributed storage with blockchain audit trails. The system underwent 30-day continuous operational validation protecting a 500-million-parameter Transformer model under real-world conditions including temperature variations (-5°C to 35°C), grid maintenance events, and concurrent SCADA traffic. RESULTS: The framework achieved 99.2% system availability with distance-dependent quantum key distribution rates ranging from 4.5 kbps (50 km) to 0.5 kbps (180 km), quantum bit error rates maintained between 3.2-11.4% within operational thresholds, hierarchical AES encryption throughput of 85 MB/s for model parameters, and storage system performance delivering 8,500-10,800 read IOPS with 1.05 ms average latency. CONCLUSION: This work validates the practical viability of quantum communication networks for securing distributed foundation models in critical power infrastructure, demonstrating information-theoretic security under operational network conditions while establishing integration protocols between quantum key distribution channels and encrypted data transmission pathways for large-scale AI model protection.
Copyright (c) 2026 Jiwu Liu, Kai Xue, Yachen Wang, Chunguang Ren, Xiaojian Zhang, Kai Han https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
INTRODUCTION: The synchronization of three-dimensional digital twin models across geographically distributed power grid control centers represents a critical challenge in modern power system operations. Traditional synchronization methods including Network Time Protocol (NTP) and Precision Time Protocol (PTP) achieve only millisecond-level accuracy, which proves fundamentally insufficient for monitoring and responding to critical power grid transients that occur on sub-millisecond timescales. The proliferation of renewable energy sources and distributed generation resources further intensifies synchronization requirements. OBJECTIVES: This research develops a comprehensive quantum communication-assisted synchronization framework designed to achieve unprecedented temporal accuracy for distributed 3D power grid models spanning multi-regional control centers while maintaining compatibility with existing infrastructure. METHODS: A hierarchical quantum-classical hybrid architecture is proposed that strategically utilizes quantum entanglement channels for time-critical synchronization signals and classical communication channels for bulk data transmission. An adaptive fault-tolerance mechanism dynamically adjusts quantum error correction strategies based on real-time quantum channel quality assessments, with graceful degradation to classical protocols when necessary. RESULTS: Extensive simulations utilizing 128 IEEE 118-bus system models demonstrate sub-10 nanosecond synchronization precision—representing 10,000-fold improvement over NTP—with end-to-end latencies maintained below 10 microseconds. The framework exhibits robust performance with synchronization success rates exceeding 90% under 15% node failure conditions and demonstrates logarithmic O(log M) time complexity compared to O(M²) scaling for classical consensus protocols. CONCLUSION: The quantum communication-assisted framework delivers superior accuracy, scalability, and fault tolerance compared to classical synchronization protocols, establishing viability for next-generation smart grids with extensive distributed energy resources.
Copyright (c) 2026 Youhui Chen, Zhonghua Lv, Ruixue Hu, Xinying Zhao, Dongxue Li https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
INTRODUCTION: The highly sensitive 3D cost estimation data in substation projects faces significant security challenges during distributed transmission, as conventional encryption approaches prove inadequate against quantum computing threats for large-scale engineering data protection. OBJECTIVES: This study aims to develop a quantum communication-based secure transmission framework for substation 3D cost estimation data, employing a three-tier quantum architecture to achieve information-theoretic security levels with robust defense against network attacks. METHODS: The proposed solution employs a three-tier quantum architecture encompassing physical, network, and application layers, with optimized quantum channels for engineering data flow. The framework utilizes the BB84 protocol with the decoy state method, implementing quantum key distribution and classical encryption integration to maintain data integrity through a distributed optimization strategy. RESULTS: Simulation validation across 10-50 node networks demonstrates quantum key generation rates of 8-40 kbps and 23% improvement in network throughput compared to star topology over traditional methods. The system achieves 99.99% reduction in key usage compared to pure quantum approaches while maintaining information-theoretic security levels. CONCLUSION: This research establishes fundamental principles and implementation pathways for quantum communication applications in engineering domains, offering significant advancement in secure data transmission for sensitive industrial applications and providing a reliable solution for protecting critical infrastructure engineering data.
Copyright (c) 2026 Junchao Li, Fei You, Dan Chen https://creativecommons.org/licenses/by-nc-sa/4.0
A Distributed and Secure Resource Allocation Method for Power Communication Networks Based on Policy Distillation
INTRODUCTION: In the next-generation smart grid communication architecture, how to achieve secure, dynamic, and fine-grained network resource allocation to ensure differentiated QoS for various services has become a key challenge. OBJECTIVES: Therefore, this study proposes a lightweight resource allocation method based on constrained policy distillation to address the challenge of balancing lightweight deployment with strong security assurance in power communication networks. METHODS: By integrating Graph Neural Networks (GNNs) and Bidirectional LSTM (Bi-LSTM), the model extracts three-dimensional features of topology, service, and resources to construct a 128-dimensional joint state representation. Moreover, a multi-objective reward function is designed that employs a double Q-network to mitigate value overestimation and generate a high-fidelity decision trajectory library. Through service-constrained policy distillation, the model innovatively combines KL divergence loss, a squared hard-constraint loss, and a soft-constraint L2 loss to compress the teacher model into a student model, subsequently compiled and deployed at the edge. Finally, a rule engine layer dynamically adjusts priorities for intercepting critical violations and ensures the security of the power system. RESULTS: Experimental results based on real-world power grid datasets demonstrate that our model achieves superior performance in resource efficiency, security, and edge effectiveness, effectively balancing lightweight deployment with strong security assurance in resource allocation for power communication networks. CONCLUSION: It can be seen that this method enables distributed and secure resource allocation in power communication network environments, thus providing reliable QoS guarantees for new-type power systems.
Distributed and Secure Resource Allocation
Copyright (c) 2026 Yue Zhang, Zongtao Li, Si Chen, Guoqiang Hu, Pengcheng Li, Ruimei Wu https://creativecommons.org/licenses/by-nc-sa/4.0
An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance
Federated learning, as a distributed machine learning paradigm, allows multiple participants to collaboratively train a shared model without sharing their local data. However, the increasing demand for privacy protection during data aggregation within distributed systems underscores the persistent challenge of ensuring both security and efficiency. Many existing Privacy-Preserving Machine Learning (PPML) schemes relying on homomorphic encryption introduce substantial computational overhead during aggregation, rendering them impractical for large-scale PPML applications involving resource-constrained participant devices. Moreover, device dropout events and data poisoning attacks perpetrated by malicious clients adversely affect the integrity of the aggregated results. To address these challenges, this paper proposes an efficient privacy-preserving secure aggregation scheme capable of tolerating participant dropout at arbitrary stages and securing data against both semi-honest and malicious participants. By integrating input verification protocols and applying gradient masking techniques, the scheme enhances its resilience against malicious attacks while ensuring user data privacy. Leveraging the additive homomorphic property of Shamir's secret sharing enables efficient global mask recovery, significantly optimizing the scheme's efficiency. Experimental results demonstrate that the proposed scheme significantly outperforms baseline methods in computational efficiency, communication overhead, and security robustness. By effectively balancing high privacy protection with practical feasibility, this scheme presents a promising solution for secure multi-party aggregation in large-scale distributed systems.
Copyright (c) 2026 Zijun Guo, Yuteng Sun, Xinyue Zhang, Lingling Wu https://creativecommons.org/licenses/by-nc-sa/4.0
FreqEdgeViT: A Scalable and Efficient Reliability-Aware Transformer for Large-Scale Agricultural Information Systems
With the exponential growth of data in modern agriculture, satellite image time series (SITS) has become an important data source for scalable information systems that analyze global crop distribution. However, processing these massive, high-dimensional data streams poses significant challenges; existing semantic segmentation models suffer from prohibitive computational overhead and lack scalability. Furthermore, they are vulnerable to high-frequency non-phenological perturbations and mixed-pixel boundary ambiguity, which degrades reliability in agricultural Internet of Things (IoT) applications. In this work, we propose FreqEdgeViT, an efficient, reliability-aware, and boundary-guided Vision Transformer. Designed for scalable SITS processing, FreqEdgeViT integrates a factorized spatiotemporal architecture with two novel mechanisms. First, in the temporal domain, we introduce a Phenology-Aware Frequency Filter (PAFF) combined with Reliability-Aware Token Merging (Ra-ToMe). This combination utilizes spectral analysis to filter environmental noise and dynamically prunes temporal redundancy based on signal reliability, significantly reducing data throughput requirements. Second, in the spatial domain, we propose a Boundary-Guided Spatial Encoder (BGSE) that enforces explicit geometric constraints to resolve edge blurring in mixed pixels. Experimental results on two public SITS datasets demonstrate that FreqEdgeViT achieves state-of-the-art accuracy with significantly reduced computational costs. The proposed architecture offers a scalable solution for processing large-scale agricultural data, providing precise support for crop policy formulation and enhancing the value of agricultural information systems.
Copyright (c) 2026 Li Qu, Le Sun, Yimin Yu, Hemant Ghayvat https://creativecommons.org/licenses/by-nc-sa/4.0
Chinese Online Violent Speech Detection Based on EBLA
INTRODUCTION: The Internet's features of transcending time and space and anonymity have fostered more rampant and covert online violent speech. Thus, accurate and effective management of online public opinion is of great significance. In recent years, scholars both domestically and internationally have conducted extensive research on online violent speech detection, but current challenges include extracting semantics from diverse and implicit expressions in Chinese online violent short texts. OBJECTIVES: This paper aims to propose the EBLA model for online violent speech detection, based on the ERNIE knowledge-enhanced semantic understanding pre-training model and the BiLSTM-Attention network, to precisely identify relevant textual semantic information and provide an effective method for online content moderators. METHODS: The model is trained using publicly available Chinese datasets related to online violence. It enhances deep, sentence-level feature extraction by integrating an attention mechanism into the BiLSTM layer on top of the ERNIE pre-training model. The model consists of vector transformation, deep text feature extraction, and text classification prediction phases. RESULTS: Results show that the precision of this model in identifying Chinese online violence tasks surpasses the BERT pre-training model by 3.7% and outperforms the BiLSTM combined with the attention mechanism by 13.84%. Empirical studies on additional datasets confirm the model's robustness and transferability. CONCLUSION: The EBLA model provides a strong basis for online violent speech detection, though it has limitations such as not accounting for identity bias or dynamic speech nature. Future improvements will focus on multimodal analysis and dynamic monitoring capabilities.
Copyright (c) 2026 Hongliang Wang, Shoumin Zhang, Na Li, Jing Liu, Peng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on video encryption algorithm based on SM4-CFB mode
INTRODUCTION: There are challenges of video data leakage, unauthorized and misconnected devices, and the potential for illegal tampering of video surveillance information in current public security IP video surveillance systems. OBJECTIVES: To solve the problems, a video encryption scheme based on the Cipher Feedback (CFB) mode of the nationally recognized SM4 algorithm was proposed. METHODS: A complete video encryption and decryption process framework was designed. To verify the effectiveness of the scheme, an experimental platform was established, and multiple test video sequences with varying characteristics were selected. Comparative experiments and analysis were conducted with the AES-CFB and DES-CFB algorithms from multiple perspectives, including subjective visual characteristics, objective statistical characteristics, encryption efficiency (and anti-interference capability. RESULTS: The experimental results demonstrate that the proposed SM4-CFB video encryption scheme effectively destroys the statistical characteristics of video data, resulting in a visually scrambled video after encryption. Its encryption efficiency is comparable to that of AES and significantly higher than that of DES, meeting the requirements of real-time video transmission and demonstrating robustness to common interference during transmission. CONCLUSION: It is a secure, efficient, and practical video encryption solution.
Copyright (c) 2026 Hongjie Tang https://creativecommons.org/licenses/by-nc-sa/4.0
Optimization of Urban Traffic Signal Control System on Hadoop Platform Based on Privacy Computing
With the acceleration of urbanization, the number of motor vehicles has surged, and problems such as urban traffic congestion, low traffic efficiency, and resource waste have become increasingly prominent. Traditional traffic signal control systems rely on fixed allocation or simple sensing control, which cannot adapt to the dynamic changes in traffic flow and cannot meet the needs of refined control. This article aims to leverage the Hadoop platform empowered by privacy computing, balance the efficiency of processing massive traffic data with data privacy and security, optimize urban traffic signal control strategies, and enhance traffic operation efficiency and intelligent control level. In terms of methods, firstly, multi-dimensional traffic data such as traffic flow, speed, and queue length are collected through traffic detectors, monitoring devices, etc., and sensitive traffic data is desensitized and encrypted using privacy computing technology. Combining HDFS distributed storage technology on the Hadoop platform to achieve secure data storage and compliant calling, utilizing components such as MapReduce and Spark to perform data cleaning, mining, and analysis under privacy protection, and constructing a traffic flow prediction model. Based on the predicted results, design a dynamic signal timing optimization algorithm to replace the traditional fixed timing mode. The results show that the optimized traffic signal control system can respond to changes in traffic flow in real time, effectively shorten the average queuing time of vehicles by 15% -25%, improve intersection traffic efficiency by about 20%, reduce vehicle idle fuel consumption and exhaust emissions, and achieve the coordinated promotion of traffic data privacy and security and data utilization. The Hadoop platform empowered by privacy computing can efficiently and securely process massive heterogeneous sensitive data in urban transportation, providing reliable data support, privacy protection, and technical support for traffic signal control optimization. The proposed optimization strategy can effectively alleviate traffic congestion and enhance the intelligence and refinement level of urban traffic control.
Copyright (c) 2026 Sun Yaping, Cao Sen https://creativecommons.org/licenses/by-nc-sa/4.0
NLP-Based Robust Object Detection and Recognition Through Multimodal Relation Graph Construction Using MPST AND DSWDCNN
INTRODUCTION: Nowadays, object detection and recognition play an important role in various applications such as surveillance, autonomous driving, robotics, and medical imaging. However, none of the traditional works focuses on analyzing the explicit relationships, logical dependencies, and semantic conflicts in text-rich or complex scenes, affecting object recognition accuracy. OBJECTIVES: A Natural Language Processing (NLP)-based robust object detection and recognition framework is developed through multimodal relation graph construction using Minimum Persistence Spanning Tree (MPST) and Deep Swim Wishart Distribution Convolutional Neural Network (DSWDCNN). METHODS: Initially, image with their corresponding captions is collected. Then, the image preprocessing and text preprocessing are done independently. From the preprocessed image, the object is detected using You Aspect-ratio Adaptive Anchors Only Look Once version-8 (YAAAOLOv8), followed by visualization. Meanwhile, from the preprocessed text, entity relations are identified. The multimodal relation graph is constructed using MPST. Further, the features from preprocessed text, relation graphs, detected objects, and visualized-images are extracted. Next, the multimodal analysis is carried out. RESULTS: In the meantime, the word embedding is performed on the preprocessed texts. Finally, the object recognition is carried out using DSWDCNN. CONCLUSION: The proposed framework achieves an object recognition accuracy of 98.8569%, demonstrating its effectiveness under weakly supervised conditions.
Human Computer Interface (HCI) Applications
Copyright (c) 2026 Bo Feng, Jiayi Yang, Jingyue Xue https://creativecommons.org/licenses/by-nc-sa/4.0
AML-TSCRA: A Machine Learning-Based Task Scheduling and Computational Resource Adaptive Allocation Method for Library Distributed Databases
INTRODUCTION: As libraries increasingly rely on distributed database systems to manage growing data volumes and service demands, conventional task scheduling and resource allocation strategies often struggle to adapt to dynamic workloads, leading to suboptimal resource utilization and performance bottlenecks. OBJECTIVES: This paper aims to develop an intelligent adaptive framework that jointly optimizes task scheduling and computational resource allocation, thereby improving responsiveness, load balancing, and overall system efficiency. METHODS: We propose AML-TSCRA (Adaptive Machine Learning-based Task Scheduling and Computational Resource Allocation), which integrates LSTM-based load forecasting with reinforcement learning-based scheduling and adaptive resource allocation based on real-time system states. RESULTS: Experiments on three representative datasets, including Amazon EC2 Logs, Google Cluster Data, and SLURM Workload Manager, show that AML-TSCRA achieves its strongest performance on Amazon EC2 Logs, with a task completion rate of 88.9%, resource utilization of 81.8%, an average response time of 1.31 s, and system throughput of 23.7 tasks/s. The model also shows moderate gains on Google Cluster Data and smaller but consistent advantages on the more heterogeneous SLURM workload. In addition, it demonstrates relatively better robustness than the learning-based baselines under noisy conditions. CONCLUSION: AML-TSCRA improves the efficiency and adaptability of distributed library-oriented service systems by combining predictive foresight, adaptive scheduling, and coordinated resource allocation. The proposed framework provides a practical basis for intelligent resource orchestration in modern data-intensive environments.
Copyright (c) 2026 Can Ma https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
One of the principal requirements for Intelligent Manufacturing Systems that are working in complicated, nonlinear, and uncertain environments is the robust real-time monitoring and an adaptive control system. In this paper, a deep reinforcement learning-based adaptive control framework is proposed that combines multi-sensor fusion, residual-based anomaly detection, and robust state estimation. The framework is tested on a multi-sensor manufacturing dataset that consists of a total of 10,000 time steps of four different heterogeneous sensor streams collected under both normal and anomalous operating conditions. The sensor data is initially preprocessed by noise filtering, normalization, synchronization, and feature extraction, after which an Unscented Kalman Filter (UKF) is used for sensor fusion and state estimation. Residual analysis along with Support Vector Machine (SVM) allows for real-time anomaly detection and classification, while Multi-Model Adaptive Estimation (MMAE) technique improves the robustness of the state estimation. A Proximal Policy Optimization (PPO) agent uses the updated system state and the anomaly information for adaptive control. The results of the experiments reveal perfect anomaly classification with an ROC-AUC of 1.000, highly stable state estimation in all state dimensions, and continuous control performance increase with the average reward of 1.79, thereby validating the proposed framework's suitability for anomaly-aware adaptive control in intelligent manufacturing systems.
Copyright (c) 2026 Shanshan Kong, Peng Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
A Cloud Environment Security Access Control Scheme Based on Federated Learning and Fuzzy Logic Integration
In cloud environments, the coexistence of multi-source heterogeneous nodes, cross-domain data sharing, and dynamic access control requirements are mutually intertwined. The lack of capability to address dynamic node risks and differentiated access demands necessitates a solution to these challenges. To this end, a cloud environment security access control scheme integrating federated learning and fuzzy logic is proposed. Firstly, using fuzzy logic to quantitatively evaluate the multidimensional dynamic attributes of nodes in the cloud environment, the results serve as a prerequisite for selecting participating nodes in federated learning; Secondly, a blockchain based federated learning architecture is constructed, and a ciphertext policy attribute based encryption algorithm is introduced to deeply couple access control policies with the federated learning process, achieving fine-grained control where only authorized nodes can participate in model aggregation and decryption. Experimental results demonstrate that this control scheme effectively evaluates the security state of the cloud environment, identifies and defends against multiple attack behaviours, achieves precise permission control for users of varying identities, and ensures the security, reliability, and dynamic adaptability of access control within the cloud environment.
Copyright (c) 2026 Hongbo Li https://creativecommons.org/licenses/by-nc-sa/4.0
Team structure optimization and talent flow prediction based on graph neural network
This paper proposes a graph neural network-based framework for team structure optimization and talent flow prediction to address the limitations of traditional methods in modeling complex organizational dynamics. By constructing an organizational graph structure containing 12,547 employees and 56,892 flow records, we develop a hybrid model combining graph attention networks and multi-layer perceptrons to accurately capture team collaboration relationships and talent mobility patterns. The proposed method achieves an F1 score of 78.6% in teamwork prediction, and 81.2% accuracy with 0.864 AUC in talent flow prediction, outperforming traditional methods by over 10%. Experimental results demonstrate that graph neural networks can effectively model complex dependencies in organizational structures, providing data-driven decision support for team optimization and talent management.
Copyright (c) 2026 Shuangshuang Chen, Huaqiang Lai Lai https://creativecommons.org/licenses/by-nc-sa/4.0
EBTM: Web Vulnerability Attack Behavior Recognition Method Based on Feature Fusion
INTRODUCTION: With the continuous expansion of network scale, effectively detecting web vulnerability attacks has become a critical challenge for ensuring network security. Existing research, primarily focused on balanced sample recognition, proves inadequate for real-world scenarios due to high false alarm rates in large-scale traffic and poor recognition performance for minority attack samples caused by imbalanced data distribution. OBJECTIVES: This paper aims to address the limitations of current web vulnerability attack detection methods in imbalanced real-world environments. The objective is to propose a novel recognition method that reduces the false positive rate and improves the identification performance for minority attack samples under highly skewed data distributions. METHODS: We propose a feature fusion-based recognition method named EBTM for imbalanced samples. The method integrates expert knowledge to optimize feature selection, focusing on key information and ensuring a more uniform mapping of URL requests. It employs three output features from different advantageous models for feature fusion, thereby generating a richer and more discriminative feature representation for the final recognition task. RESULTS: Experimental results demonstrate that the proposed EBTM method significantly enhances the recognition of web vulnerability attack behaviors. Under a realistic imbalanced condition where attack samples constitute only about 3% of the data, the model achieves a macro-average F1 score of 99.1% and reduces the false positive rate to 0.054%. CONCLUSION: The EBTM method effectively improves the efficiency and accuracy of web vulnerability attack behavior recognition in practical, imbalanced scenarios. By combining expert-guided feature optimization and multi-model feature fusion, it successfully addresses key challenges of high false alarms and poor minority class recognition, offering a robust solution for securing large-scale network environments.
Copyright (c) 2026 Xiangnan Lin, Bin Lu https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Implementation of a Collaborative Filtering Algorithm Based on Deep Learning and Matrix Factorization
INTRODUCTION: Collaborative filtering (CF) algorithms based on deep learning and matrix factorization aim to learn deeper latent features of users and items from user ratings. However, such methods often face challenges due to the sparsity of rating data, which limits their recommendation performance. OBJECTIVES: This study aims to design a hybrid recommendation algorithm named auto-encoder deep learning and matrix factorization (AED-MF), which integrates deep learning and matrix factorization to address the sparsity issue in rating data and improve the extraction of deeper latent features of users and items. METHODS: The AED-MF algorithm combines an auto-encoder-based deep learning approach with matrix factorization to model both deeper hidden representations and nonlinear relationships between users and items. The methodology includes data downloading, mounting, cleaning, model training, and experimental evaluation, with matrix factorization applied to predict missing ratings in the rating matrix. RESULTS: The proposed AED-MF algorithm demonstrated strong performance in key recommendation metrics, effectively learning deeper user and item representations and applying them to capture complex nonlinear relationships. It also reduced the impact of rating data sparsity while maintaining high recommendation accuracy. CONCLUSION: The AED-MF recommendation algorithm successfully alleviates the problem of sparse rating data and enhances recommendation accuracy by leveraging the strengths of both deep learning and matrix factorization, offering an effective solution for improving collaborative filtering-based recommender systems.
Copyright (c) 2026 Meiyu Fang, Zhe Zhu, Leyi Cai, Zelin Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Security-Aware Scheduling Methods for Distributed Systems with Integrated Motion Data Privacy Protection
INTRODUCTION: With the growth of Internet of Things (IoT) and edge computing, distributed systems are increasingly deployed across fields such as sports health and intelligent transportation. However, motion data, often containing sensitive personal information, poses significant privacy risks when handled in these systems. OBJECTIVES: This paper aims to propose a novel security-aware scheduling method that integrates motion data privacy protection in distributed systems. The goal is to balance system scheduling efficiency with robust privacy safeguards. METHODS: We introduce a framework that combines encryption technologies, privacy protocols, and dynamic scheduling algorithms. By embedding privacy protection constraints into the scheduling process, this method optimizes data transmission and storage during task execution. RESULTS: Experimental results demonstrate that the proposed approach effectively reduces privacy leakage risks by over 80% compared to classical greedy algorithms. While the mandatory cryptographic mechanisms introduce a marginal latency overhead, the system maintains highly competitive scheduling efficiency. When compared with state-of-the-art techniques such as pure DRL, the proposed system achieves a 71% reduction in privacy leakage probability, successfully balancing robust security with dynamic adaptability. CONCLUSION: This research presents an innovative solution for motion data privacy protection in distributed systems, offering significant improvements in both privacy and scheduling performance. The method's applicability extends to fields like IoT, smart health, and intelligent transportation, marking a crucial step toward more secure and efficient distributed systems.
Copyright (c) 2026 Zengming Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
A Point Cloud Instance Segmentation Framework with Attention Mechanisms and Semantic Refinement
INTRODUCTION: Point cloud instance segmentation, a critical 3D computer vision task, faces significant challenges in complex indoor environments. Methods often suffer from insufficient feature extraction and a strong coupling between semantic prediction and instance features, where semantic errors cascade and limit overall accuracy. OBJECTIVES: This paper proposes an innovative approach using attention mechanisms and semantic refinement to address these limitations. The primary goal is to enhance feature representation and alleviate the strong dependency between semantic prediction and instance segmentation. METHODS: We introduce three key innovations: 1) A reverse attention mechanism to improve multi-level feature fusion; 2) An instance soft clustering strategy incorporating semantic scores to weaken the semantic-instance coupling; and 3) A self-attention-based instance refinement network. Finally, a dual-branch scoring mechanism, combining classification and mask scores, jointly determines confidence levels to further mitigate semantic errors. RESULTS: Evaluated on the S3DIS dataset, our model achieved 74.1% mean Precision(mPrec) on the Area 5 test. In the more rigorous six-fold cross-validation, it achieved 76.8% mPrec and 72.3% mean recall rate(mRec), outperforming the state-of-the-art (SOTA) model SoftGroup by 1.5% and 2.5%, respectively. CONCLUSION: The proposed method significantly improves instance segmentation accuracy and demonstrates stronger robustness in complex scenes. It effectively resolves the strong coupling issue, providing a novel technical pathway for point cloud instance segmentation. While primarily optimized for dense indoor scans, adapting this framework for extremely sparse outdoor point clouds remains a compelling direction for future exploration.
Copyright (c) 2026 Xi Chen, Meiji Chen, Hao Lin https://creativecommons.org/licenses/by-nc-sa/4.0
The Optimization of Load Balancing Strategies in Heterogeneous Distributed Environments for Regional Industrial Upgrading
INTRODUCTION: With the ongoing advancement of regional industrial upgrading, efficient load balancing in heterogeneous distributed environments has emerged as a pivotal technical challenge. Conventional load balancing methods, typically designed for homogeneous systems or based on static scheduling rules, are ill-equipped to handle the dynamic task demands and diverse resource capabilities characteristic of modern industrial computing infrastructures. OBJECTIVES: This study seeks to bridge this gap by developing an intelligent, adaptive load balancing strategy specifically tailored for heterogeneous environments, with the goal of maximizing resource utilization, minimizing response latency, and supporting sustainable industrial transformation. METHODS: The proposed framework synergistically combines real-time dynamic resource monitoring, multi-dimensional task characterization and scheduling, and reinforcement learning-driven decision-making to construct a responsive and self-optimizing load distribution mechanism that continuously adapts to system state changes. RESULTS: Comprehensive experiments show that the approach significantly outperforms traditional strategies (e.g., round-robin, shortest job first) and state-of-the-art deep reinforcement learning methods across key performance metrics (p < 0.05), achieving up to a 44% reduction in average response time and a remarkable 91.4% system-wide resource utilization. CONCLUSION: By enhancing both adaptability and efficiency in complex heterogeneous settings, the proposed strategy offers a practical and scalable solution with strong potential for deployment in smart manufacturing, cloud computing, edge computing, and other industrial digitalization scenarios.
Copyright (c) 2026 Zhigang Guo, Zhen Li https://creativecommons.org/licenses/by-nc-sa/4.0