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FLock: Defending Malicious Behaviors in Federated Learning with Blockchain. (arXiv:2211.04344v1 [cs.CR])
Nov. 9, 2022, 2:20 a.m. | Nanqing Dong, Jiahao Sun, Zhipeng Wang, Shuoying Zhang, Shuhao Zheng
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) is a promising way to allow multiple data owners
(clients) to collaboratively train machine learning models without compromising
data privacy. Yet, existing FL solutions usually rely on a centralized
aggregator for model weight aggregation, while assuming clients are honest.
Even if data privacy can still be preserved, the problem of single-point
failure and data poisoning attack from malicious clients remains unresolved. To
tackle this challenge, we propose to use distributed ledger technology (DLT) to
achieve FLock, a …
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