Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To avoid the single point of failure problem in FL, decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation, which has been considered an attractive solution for machine learning tasks on distributed personal devices. However, this process is vulnerable to attackers who share false models and data. If there exists a group of malicious clients, they might harm the performance of the model by carrying out a poisoning attack. In addition, in DFL, clients often lack the incentives to contribute their computing powers to do model training. In this paper, we proposed Blockchain-based Decentralized Federated Learning (BDFL), which leverages a blockchain for decentralized model verification and auditing. BDFL includes an auditor committee for model verification, an incentive mechanism to encourage the participation of clients, a reputation model to evaluate the trustworthiness of clients, and a protocol suite for dynamic network updates. Evaluation results show that, with the reputation mechanism, BDFL achieves fast model convergence and high accuracy on real datasets even if there exist 30\% malicious clients in the system.

翻译：联邦学习（FL）是一种在移动和物联网设备上实现分布式机器学习的经典范式，能够在保护数据隐私的同时优化通信效率。为避免联邦学习中的单点故障问题，研究者提出了去中心化联邦学习（DFL），通过点对点通信进行模型聚合，这被认为是分布式个人设备上机器学习任务的一种有吸引力的解决方案。然而，该过程容易受到攻击者共享虚假模型和数据的威胁。若存在恶意客户端群体，他们可能通过发起投毒攻击损害模型性能。此外，在去中心化联邦学习中，客户端往往缺乏贡献自身算力进行模型训练的激励。本文提出了基于区块链的去中心化联邦学习（BDFL），利用区块链实现去中心化的模型验证与审计。BDFL包含用于模型验证的审计委员会、激励客户端参与的激励机制、评估客户端可信度的声誉模型，以及用于动态网络更新的协议套件。评估结果表明，即使系统中存在30%的恶意客户端，带有声誉机制的BDFL仍能在真实数据集上实现快速模型收敛和高准确率。