Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
DOI:
https://doi.org/10.4108/eetsis.12254Keywords:
federated learning algorithm, edge computing, communication privacy protection, local model, edge aggregation, adaptive differentialAbstract
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.
References
[1] Abou El Houda Z, Moudoud H, Brik B, Khoukhi L. Blockchain-enabled federated learning for enhanced collaborative intrusion detection in vehicular edge computing. IEEE Trans Intell Transp Syst. 2024; 25(7):7661-7672.
[2] Zhang L, Wen F, Zhang Q, Gui G, Sari H, Adachi F. Constrained multiobjective decomposition evolutionary algorithm for uav-assisted mobile edge computing networks. IEEE Internet Things J. 2024; 11(22):36673-36687.
[3] Asheralieva A, Niyato D, Miyanaga Y. Efficient dynamic distributed resource slicing in 6g multi-access edge computing networks with online admm and message passing graph neural networks. IEEE Trans Mob Comput. 2024; 23(4):2614-2638.
[4] Jha M K, Kumar M. An autonomic offloading and resource allocation technique for IoT applications in edge computing. J Supercomput. 2025; 81(2):360.
[5] Luo Z, Amayri M, Fan W, Bouguila N. Cross-collection latent beta-liouville allocation model training with privacy protection and applications. Appl Intell. 2023; 53(14):17824-17848.
[6] Kaur J, Rani R, Kalra N. Healthcare data security and privacy protection framework based on dual channel blockchain. Trans Emerg Telecommun Technol. 2025; 36(1):e70049-e70068.
[7] Baawi S S, Oleiwi Z C, Al-Muqarm A M A, Al-Shammary D, Sufi F. Efficient malware detection based on machine learning for enhanced cloud privacy protection. Evol Syst. 2025; 16(1):30.
[8] Saravanan P S, Ramani S, Reddy V R F Y. A novel approach of privacy protection of mobile users while using location-based services applications. Ad Hoc Netw. 2023; 149:103253.
[9] Ranjan A K, Kumar P. APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things. Ad Hoc Netw. 2024; 164:103631.
[10] Shang W, Ge J, Ding L, Jiang Z, Sui H. Acceleration offloading for differential privacy protection based on federated learning in edge intelligent controllers. Future Gener Comput Syst. 2025; 163:107526.
[11] Qu Z, Zhang L, Tiwari P. Quantum fuzzy federated learning for privacy protection in intelligent information processing. IEEE Trans Fuzzy Syst. 2025; 33(1):278-289.
[12] Herath C, Liu X, Lambotharan S, Rahulamathavan Y. Enhancing federated learning convergence with dynamic data queue and data-entropy-driven participant selection. IEEE Internet Things J. 2025; 12(6):6646-6658.
[13] Lee S, Zhang T, Prakash S, Niu Y, Avestimehr S. Embracing federated learning: enabling weak client participation via partial model training. IEEE Trans Mob Comput. 2024; 23(12):11133-11143.
[14] Jadav N K, Tanwar S. Whale optimization-orchestrated Federated Learning-based resource allocation scheme for D2D communication. Ad Hoc Netw. 2024; 163:103565.
[15] Ge H, Pokhrel S R, Liu Z, Wang J, Li G. PFL-DKD: Modeling decoupled knowledge fusion with distillation for improving personalized federated learning. Comput Netw. 2024; 254:110758.
[16] Hu X, Cai H, Alazab M, Zhou W, Haghighi M S, Wen S. Federated learning in industrial iot: a privacy-preserving solution that enables sharing of data in hydrocarbon explorations. IEEE Trans Ind Inf. 2024; 20(3 Pt 2):4337-4346.
[17] Al Shahrani A M, Rizwan A, Sánchez-Chero M, Cornejo L L C, Shabaz M. Blockchain-enabled federated learning for prevention of power terminals threats in iot environment using edge zero-trust model. J Supercomput. 2024; 80(6):7849-7875.
[18] Bukhari S M S, Zafar M H, Abou Houran M, Moosavi S K R, Mansoor M, Muaaz M, Sanfilippo F. Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability. Ad Hoc Netw. 2024; 155:103407.
[19] Djukic M, Proki I, Popovic M, Ghilezan S, Popovic M, Proki S. Correct orchestration of federated learning generic algorithms: python translation to csp and verification by pat. Int J Softw Tools Technol Transf. 2025; 27(1):21-34.
[20] Fu T D, Li Y Q. Privacy Protection of Big Data in Complex Network Based on Federated Learning Algorithm. Comput Simul. 2024; 41(6):498-502.
[21] Mafeni V, Kim Y. An automated edge computing approach for iot device registration and application deployment. IEEE Syst J. 2024; 18(2):1447-1458.
[22] Liu F, Liu H, Kannadasan R, Jiang Q. A biometric-based implicit authentication protocol with privacy protection for ubiquitous communication environments. Int J Commun Syst. 2025; 38(1):e5578.
[23] Sarkar S, Agrawal S, Chowdhuri A, Ramani S. Progressive search personalization and privacy protection using federated learning. Expert Syst. 2025; 42(1):e13318.
[24] Zhang J, Si K, Zeng Z, Li T, Ye X. Iea-dp: information entropy-driven adaptive differential privacy protection scheme for social networks. J Supercomput. 2024; 80(14):20256-20582.
[25] Tian X, Du X, Liu X, Wang L, Zhao L. A low-delay source-location-privacy protection scheme with multi-auv collaboration for underwater acoustic sensor networks. IEEE Sens J. 2025; 25(7):12236-12252.
[26] Pei N, Wan B F, Xie S X, Zhang T H, Zhang H F. Yellow light privacy protection with anti-reflection structure based on photonic band gap principle. J Opt. 2024; 26(6):065104.
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