A systematic review of secure federated learning based on blockchain and Multi-Party computation

Abstract

Federated Learning enables collaborative model training without compromising data privacy. However, security concerns remain, particularly regarding participant contributions and model integrity. This paper explores the potential of integrating Blockchain and Multi-Party Computation techniques to address these challenges in Federated Learning. We systematically review recent research works on examining the capabilities of Blockchain-based Federated Learning and multi-party computation in mitigating security threats in federated learning, such as data leakage and model poisoning. In addition, by analysing the convergence of these technologies, we aim to provide insights into their potential for building more secure, trustworthy, and privacy-preserving Federated Learning. We conclude the review by identifying open research questions and outlining promising directions for future research in this area, as this convergence is not only a technical achievement but a foundational one towards democratised, secure, and privacy-aware artificial intelligence.

Keywords Blockchain-Based decentralization, Decentralized trust mechanisms, Federated learning security, Model integrity verification, Privacy-Preserving machine learning, Secure Multi-Party computation