Wang Rong, Tsai Wei-Tek
Digital Society & Blockchain Laboratory, Beihang University, Beijing 100191, China.
Sensors (Basel). 2022 Feb 21;22(4):1672. doi: 10.3390/s22041672.
The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault tolerance. In this paper, we propose an asynchronous federated learning system based on permissioned blockchains, using permissioned blockchains as the federated learning server, which is composed of a main-blockchain and multiple sub-blockchains, with each sub-blockchain responsible for partial model parameter updates and the main-blockchain responsible for global model parameter updates. Based on this architecture, a federated learning asynchronous aggregation protocol based on permissioned blockchain is proposed that can effectively alleviate the synchronous federated learning algorithm by integrating the learned model into the blockchain and performing two-order aggregation calculations. Therefore, the overhead of synchronization problems and the reliability of shared data is also guaranteed. We conducted some simulation experiments and the experimental results showed that the proposed architecture could maintain good training performances when dealing with a small number of malicious nodes and differentiated data quality, which has good fault tolerance, and can be applied to edge computing scenarios.
现有的联邦学习框架基于集中式模型协调器，仍然面临着诸如设备计算能力差异、单点故障、隐私性差以及缺乏拜占庭容错等严峻的安全挑战。在本文中，我们提出了一种基于许可区块链的异步联邦学习系统，将许可区块链用作联邦学习服务器，该服务器由一个主区块链和多个子区块链组成，每个子区块链负责部分模型参数更新，主区块链负责全局模型参数更新。基于此架构，提出了一种基于许可区块链的联邦学习异步聚合协议，通过将学习到的模型集成到区块链中并执行二阶聚合计算，能够有效缓解同步联邦学习算法的问题。因此，同步问题的开销和共享数据的可靠性也得到了保证。我们进行了一些模拟实验，实验结果表明，所提出的架构在处理少量恶意节点和差异化数据质量时能够保持良好的训练性能，具有良好的容错能力，并且可以应用于边缘计算场景。
