FLock: Defending Malicious Behaviors in Federated Learning with Blockchain

Federated learning (FL) is a promising way to allow multiple data owners (clients)to collaboratively train machine learning models without compromising data pri-vacy. Yet, existing FL solutions usually rely on a centralized aggregator for modelweight aggregation, while assuming clients are honest. Even if data privacy canstill be preserved, the problem of single-point failure and data poisoning attackfrom malicious clients remains unresolved. To tackle this challenge, we propose touse distributed ledger technology (DLT) to achieve FLock, a secure and reliabledecentralized Federated Learning system built on blockchain. To guarantee modelquality, we design a novel peer-to-peer (P2P) review and reward/slash mechanismto detect and deter malicious clients, powered by on-chain smart contracts. The re-ward/slash mechanism, in addition, serves as incentives for participants to honestlyupload and review model parameters in the FLock system. FLock thus improvesthe performance and the robustness of FL systems in a fully P2P manner.