Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions
원문 초록
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.
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FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
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