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.

翻译：在深度学习时代，联邦学习提供了一种有前景的方法，允许多机构数据所有者（即客户）在不损害数据隐私的情况下协同训练机器学习模型。然而，现有大多数联邦学习方法依赖集中式服务器进行全局模型聚合，这导致单点故障问题，使系统在应对不诚实客户时易受恶意攻击。本研究提出一种基于区块链与分布式账本技术的安全可靠联邦学习系统来解决这一问题。该系统通过链上智能合约驱动，集成了点对点投票机制与奖惩机制，用于检测并遏制恶意行为。通过理论分析与实证评估，证明了所提方法的有效性，表明我们的框架对客户端的恶意行为具有鲁棒性。