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FLock: Defending Malicious Behaviors in Federated Learning with Blockchain

Nanqing Dong, Jiahao Sun, Zhipeng Wang, Shuoying Zhang, Shuhao Zheng

Submitted on 5 November 2022

Abstract

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 secure and reliable decentralized Federated Learning system built on blockchain. To guarantee model quality, we design a novel peer-to-peer (P2P) review and reward/slash mechanism to detect and deter malicious clients, powered by on-chain smart contracts. The reward/slash mechanism, in addition, serves as incentives for participants to honestly upload and review model parameters in the FLock system. FLock thus improves the performance and the robustness of FL systems in a fully P2P manner.

Preprint

Comment: Accepted by NeurIPS 2022 Workshop

Subjects: Computer Science - Cryptography and Security; Computer Science - Artificial Intelligence; Computer Science - Computer Science and Game Theory; Computer Science - Machine Learning

URL: http://arxiv.org/abs/2211.04344