arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.00773cs.DCcs.LG

A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

Sun Yat-sen University(中山大学);WeBank Co. Ltd.(微众银行股份有限公司)

Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, Qiang Yan

英文摘要:

Federated learning has been widely studied and applied to various scenarios. In mobile computing scenarios, federated learning protects users from exposing their private data, while cooperatively training the global model for a variety of real-world applications. However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack to the global model or user privacy data. To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i.e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC). The framework uses blockchain for the global model storage and the local model update exchange. To enable the proposed BFLC, we also devised an innovative committee consensus mechanism, which can effectively reduce the amount of consensus computing and reduce malicious attacks. We then discussed the scalability of BFLC, including theoretical security, storage optimization, and incentives. Finally, we performed experiments using real-world datasets to verify the effectiveness of the BFLC framework.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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