Decentralized Federated Learning with Differential Privacy using Threshold Cryptography

Abstract

Federated learning (FL) presents a promising approach to training machine learning models on decentralized data sources while preserving user privacy. However, traditional FL methods often rely on a central server, introducing a single point of failure and potential privacy risks. This paper proposes a novel decentralized federated learning system that leverages threshold cryptography and differential privacy to address these concerns. The core claim is that protecting user privacy in federated learning remains a significant challenge, and this system provides a robust solution. The system operates through a series of distributed rounds where participants collaboratively update model parameters using threshold cryptography to ensure secure aggregation and differential privacy to mitigate individual data exposure. This approach eliminates the need for a central server, enhancing both privacy and security. The system is designed for scalability and adaptability, making it suitable for various decentralized data scenarios. This work contributes to the development of more secure and privacy-preserving federated learning solutions.