Many researchers have proposed replacing the aggregation server in federated learning with a blockchain system to improve privacy, robustness, and scalability. In this approach, clients would upload their updated models to the blockchain ledger and use a smart contract to perform model averaging. However, the significant delay and limited computational capabilities of blockchain systems make it inefficient to support machine learning applications on the blockchain. In this paper, we propose a new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. Our architecture inherits the merits of traditional blockchain systems while achieving low latency and low resource consumption by waiving global consensus. To evaluate the performance and robustness of our architecture, we implemented a prototype and tested it on a physical four-node network, and also developed a simulator to simulate larger networks and more complex situations. Our experiments show that the DFL architecture can reach over 90\% accuracy for non-I.I.D. datasets, even in the presence of model poisoning attacks, while ensuring that the blockchain part consumes less than 5\% of hardware resources.

翻译：许多研究者提出用区块链系统替换联邦学习中的聚合服务器，以增强隐私性、鲁棒性和可扩展性。在这种方法中，客户端将更新后的模型上传至区块链账本，并通过智能合约执行模型平均。然而，区块链系统显著的延迟和有限的计算能力，使得其在支持机器学习应用时效率低下。本文提出了一种名为DFL的新型公有链架构，该架构专为分布式联邦机器学习优化。我们的架构继承了传统区块链系统的优势，同时通过放弃全局共识实现了低延迟和低资源消耗。为评估其性能与鲁棒性，我们实现了原型系统并在物理四节点网络上进行测试，同时开发了模拟器以模拟更大规模网络和更复杂场景。实验表明，即使面对模型投毒攻击，DFL架构在非独立同分布数据集上仍能达到90%以上的准确率，同时确保区块链部分消耗的硬件资源低于5%。