Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems

Abstract

Recently, Federated Learning (FL) gained considerable popularity as it offers an isolated and privacy-preserving mechanism to train Machine Learning models on unseen data. However, the use of the cloud server to build the global model might raise fairness and trust concerns since any FL server might try to regenerate the original data of some users. In this chapter, we review the key trust requirements for Decentralized Federated Learning (DFL) and provide the analysis in terms of fairness, trust, and privacy. We also present and compare the existing blockchain solutions for the development of fair and trustworthy FL systems.