  In the era of deep learning, federated learning (FL) presents a promising
approach that allows multi-institutional data owners, or clients, to
collaboratively train machine learning models without compromising data
privacy. However, most existing FL approaches rely on a centralized server for
global model aggregation, leading to a single point of failure. This makes the
system vulnerable to malicious attacks when dealing with dishonest clients. In
this work, we address this problem by proposing a secure and reliable FL system
based on blockchain and distributed ledger technology. Our system incorporates
a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are
powered by on-chain smart contracts, to detect and deter malicious behaviors.
Both theoretical and empirical analyses are presented to demonstrate the
effectiveness of the proposed approach, showing that our framework is robust
against malicious client-side behaviors.
