Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions

Abstract

Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are many challenging issues in federated learning, such as coordinating participants’ activities, arbitrating their benefits, and aggregating models. Most existing solutions employ a centralized approach, in which a trustworthy central authority is needed for coordination. Such an approach incurs many disadvantages, including vulnerability to attacks, lack of credibility, and difficulty in calculating rewards. Recently, blockchain was identified as a potential solution for addressing the abovementioned issues. Extensive research has been conducted, and many approaches, methods, and techniques have been proposed. There is a need for a systematic survey to examine how blockchain can empower federated learning. Although there are many surveys on federated learning, few of them cover blockchain as an enabling technology. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning (BlockFed). First, we identify the critical issues in federated learning and explain why blockchain provides a potential approach to addressing these issues. Second, we categorize existing system models into three classes: decoupled, coupled, and overlapped, according to how the federated learning and blockchain functions are integrated. Then we compare the advantages and disadvantages of these three system models, regard the disadvantages as challenging issues in BlockFed, and investigate corresponding solutions. Finally, we identify and discuss the future directions, including open problems in BlockFed.