EPFed: Achieving Optimal Balance between Privacy and Efficiency in Federated Learning
Electronics(2024)
摘要
Federated learning (FL) is increasingly challenged by security and privacy concerns, particularly vulnerabilities exposed by malicious participants. There remains a gap in effectively countering threats such as model inversion and poisoning attacks in existing research. To address these challenges, this paper proposes the Effective Private-Protected Federated Learning Aggregation Algorithm (EPFed), a framework that utilizes a blockchain platform, homomorphic encryption, and secret sharing to fortify the data privacy and computational efficiency in a federated learning environment. EPFed works by establishing “trust groups” through the unique integration of a Chinese Remainder Theorem-based secret sharing scheme with Paillier homomorphic encryption, streamlining secure model parameter exchange and aggregation while minimizing the computational load. Our performance-driven aggregation strategy leverages local performance metrics to safeguard against malicious contributions, ensuring both the integrity and efficiency of the learning process. The evaluations demonstrate that EPFed achieves a remarkable accuracy rate of 92.5%, thereby confirming the advanced nature of the proposed solution in addressing the pressing challenges of FL.
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