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.
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