Blockchain-empowered Federated Learning: Benefits, Challenges, and
  Solutions
Zeju Cai Jianguo Chen Yuting Fan Zibin Zheng Keqin Li
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.