DOI: 10.3390/electronics13061028 ISSN: 2079-9292

EPFed: Achieving Optimal Balance between Privacy and Efficiency in Federated Learning

Dong Mao, Qiongqian Yang, Hongkai Wang, Zuge Chen, Chen Li, Yubo Song, Zhongyuan Qin
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering

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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