Distributed Federated Learning with Byzantine Fault Tolerance using Homomorphic Encryption

Abstract

Federated learning (FL) offers a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, FL systems are vulnerable to Byzantine faults, where malicious participants introduce corrupted model updates, compromising the integrity of the global model. This paper proposes a novel framework for distributed federated learning with Byzantine fault tolerance, leveraging the power of homomorphic encryption. The system encrypts model updates using homomorphic encryption, allowing the central server to perform aggregation computations directly on the encrypted data. This approach eliminates the need for decryption, thereby safeguarding client data and providing robust protection against Byzantine attacks. The core claim of this work is that the combination of FL with homomorphic encryption provides a fundamentally secure and resilient solution for distributed model training. We detail the system architecture, the encryption and aggregation protocols, and analyze the security and performance implications. The proposed method significantly enhances the robustness of FL against malicious participants, offering a practical solution for privacy-sensitive applications.