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.

提出了基于区块链的分布式联邦学习（BDFL），利用区块链进行分散化模型验证和审计，包括审核委员会、激励机制、信誉模型和动态网络更新协议。评估结果表明，BDFL 在存在 30％恶意客户端的系统中，通过信誉机制实现了快速模型收敛和高准确性。