Decentralized Federated Learning with Differential Privacy and Blockchain Auditing

Abstract

This paper proposes a novel decentralized federated learning (DFL) system designed to address the critical challenges of privacy and accountability inherent in collaborative learning environments. The system leverages the strengths of three key technologies: decentralized learning, differential privacy, and blockchain auditing. Traditional federated learning, while improving privacy by training models locally, still relies on a central server for aggregation, creating a single point of failure and potential vulnerability. This proposed architecture eliminates the central server, distributing the learning process across multiple participants. Differential privacy mechanisms are incorporated to further protect individual data contributions, adding noise to the model updates to obscure sensitive information. Finally, a blockchain-based auditing system provides a transparent and immutable record of all model updates, ensuring accountability and allowing for verification of the learning process. This combination offers a robust, secure, and auditable solution for distributed machine learning. The system's core claim is that ensuring privacy and accountability in federated learning is a major challenge, and this architecture directly addresses this need.