Research Article
Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation
@ARTICLE{10.4108/eetsis.14118, author={Geng Cheng and Jianbo Liu}, title={Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={13}, number={1}, publisher={EAI}, journal_a={SIS}, year={2026}, month={7}, keywords={Federated Learning, heterogeneous edge computing, data element circulation, dynamic clustering technology for device performance, adaptive gradient transmission technology, data distribution alignment technology}, doi={10.4108/eetsis.14118} }- Geng Cheng
Jianbo Liu
Year: 2026
Intelligent and secure Federated Learning for data elements circulation in heterogeneous edge computation
SIS
EAI
DOI: 10.4108/eetsis.14118
Abstract
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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