Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation
DOI:
https://doi.org/10.4108/eetsis.14118Keywords:
Federated Learning, heterogeneous edge computing, data element circulation, dynamic clustering technology for device performance, adaptive gradient transmission technology, data distribution alignment technologyAbstract
INTRODUCTION: Heterogeneous edge computing creates data islands due to privacy and environmental heterogeneity. Current solutions lack an overall approach. OBJECTIVES: This study constructs a federated learning framework for secure data element circulation, mitigating low-power node tailing, balancing communication with accuracy, and adapting to non-IID data, advancing intelligent systems and cybersecurity. METHODS: The framework fuses Dynamic Clustering, Adaptive Gradient Transmission, and Data Distribution Alignment. Validation uses simulations against existing technologies. RESULTS: The proposed method achieves 0.892±0.021 comprehensive performance, 11.1%–13.6% higher than existing technologies. Under 20% network interruption, attenuation is 3.3% vs. 8.6%–12.5%. Data flow reaches 18.6±0.7 MB/s; privacy leakage is 0.8±0.2 bit. CONCLUSION: This study provides reliable support for safe, efficient data element circulation, advancing intelligent systems and cybersecurity.
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