SecureBFL : a Blockchain-enhanced federated learning architecture with MPC

Abstract

The increasing demand for data in machine learning raises significant privacy concerns. Federated Learning (FL) enables multiple entities to train models collaboratively without sharing raw data. However, centralized FL (CFL) relies on a central server, making it vulnerable to poisoning attacks and single points of failure (SPOF). Decentralized FL (DFL) addresses these issues by removing the central server. This paper proposes a novel DFL architecture integrating blockchain for resisting attacks and Multi-Party Computation (MPC) for secure model parameter transfer. This architecture enhances security and confidentiality in collaborative learning without compromising result quality.