Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions
It provides a comprehensive overview for researchers and practitioners working on improving privacy and security in distributed machine learning systems, but is incremental as a survey.
This survey reviews the integration of blockchain into federated learning to address limitations like single points of failure and lack of incentives, analyzing benefits such as enhanced security and challenges like increased resource demands.
Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.