Qammar Attia, Karim Ahmad, Ning Huansheng, Ding Jianguo
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Department of Information Technology, Bahauddin Zakariya University, Multan, Pakistan.
Artif Intell Rev. 2023;56(5):3951-3985. doi: 10.1007/s10462-022-10271-9. Epub 2022 Sep 16.
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.
联邦学习（FL）是一种很有前景的分布式机器学习框架，它在保护隐私的同时，无需共享本地数据即可训练模型。联邦学习利用协作学习的概念构建隐私保护模型。然而，联邦学习的整体特性存在诸多问题，比如私人信息泄露、向服务器上传模型参数的不可靠性、通信成本等。区块链作为一种去中心化技术，能够在无需中央服务器的情况下提升联邦学习的性能，还能解决上述问题。本文对区块链与联邦学习整合进行了系统的文献综述，并分析了现有联邦学习中可被弥补的问题。通过仔细筛选，纳入了大多数相关研究，研究问题涵盖了传统联邦学习中可由区块链解决的潜在安全和隐私攻击以及基于区块链的联邦学习的特点。此外，还深入研究了基于区块链的联邦学习在安全与隐私、记录与奖励以及验证与问责方面的最新方法。此外，还讨论了与区块链和联邦学习结合相关的开放性问题。最后，提出了基于区块链的联邦学习系统稳健发展的未来研究方向。
