Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

arXiv:2403.00873v1 Announce Type: new
Abstract: 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 …

arxiv benefits blockchain challenges clients cs.cr cs.lg data data privacy distributed empowered failure federated federated learning incentives limitations locally machine machine learning parameter points privacy security server single solutions systems training user data