【Author】
Wang, Rong; Tsai, Wei-Tek
【Abstract】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.
【Keywords】asynchronous federated learning; permissioned blockchains; privacy protection; IoT; multi-blockchains architecture
【摘要】现有的联邦学习框架基于中心化模型协调器，仍面临设备差异化计算能力、单点故障、隐私性差、缺乏拜占庭容错等严重的安全挑战。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，以许可区块链作为联邦学习服务器，由一条主链和多个子链组成，每个子链负责部分模型参数更新以及负责全局模型参数更新的主链。基于该架构，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进行二阶聚合计算，可以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。我们进行了一些仿真实验，实验结果表明，该架构在处理少量恶意节点和差异化数据质量时仍能保持良好的训练性能，具有良好的容错性，可应用于边缘计算场景。
【关键词】异步联邦学习；许可的区块链；隐私保护;物联网；多区块链架构
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