Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption

Abstract

This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform computations directly on the encrypted data without ever needing to access the plaintext. Subsequently, the encrypted results are sent back to the participants. This architecture effectively mitigates privacy risks associated with traditional federated learning methods. The combination of these three technologies – federated learning, differential privacy, and homomorphic encryption – provides a powerful framework for secure and decentralized machine learning. We demonstrate the potential for achieving strong privacy guarantees while maintaining the benefits of distributed learning.