Federated Learning in Heterogeneous Edge Computing: A Secure and Privacy-Preserving Model Aggregation Approach
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P102Keywords:
Federated Learning, Edge Computing, Model Aggregation, Privacy-Preserving, Homomorphic Encryption, Secure Multi-Party Computation, Communication Efficiency, Heterogeneity, Model Convergence, Cryptographic TechniquesAbstract
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across multiple decentralized edge devices while preserving data privacy. However, the heterogeneity of edge computing environments poses significant challenges in terms of resource allocation, communication efficiency, and model convergence. This paper proposes a novel Federated Learning framework, Secure and Privacy-Preserving Model Aggregation (SPPMA), specifically designed for heterogeneous edge computing environments. SPPMA leverages advanced cryptographic techniques and optimized communication protocols to ensure secure and efficient model aggregation. We evaluate SPPMA through extensive simulations and real-world experiments, demonstrating its effectiveness in improving model accuracy, reducing communication overhead, and enhancing privacy. The results show that SPPMA outperforms existing approaches in various heterogeneous edge computing scenarios
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References
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