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Computer Science > Cryptography and Security

arXiv:2403.00873 (cs)
[Submitted on 1 Mar 2024 (v1), last revised 5 Jul 2024 (this version, v2)]

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

Authors:Zeju Cai, Jianguo Chen, Yuting Fan, Zibin Zheng, Keqin Li
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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 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.
Comments: This work has been submitted to the IEEE for possible publication
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as: arXiv:2403.00873 [cs.CR]
  (or arXiv:2403.00873v2 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2403.00873

Submission history

From: Zeju Cai [view email]
[v1] Fri, 1 Mar 2024 07:41:05 UTC (9,263 KB)
[v2] Fri, 5 Jul 2024 09:36:26 UTC (2,881 KB)
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