【Author】
Jin, Hai; Dai, Xiaohai; Xiao, Jiang; Li, Baochun; Li, Huichuwu; Zhang, Yan
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】Federated learning (FL) has been gaining popularity as a way to provide privacy-preserving data sharing for the Internet of Medical Things (IoMT). As a complementary, blockchain technology is used in recent literature to make FL secure. However, existing blockchain-based FL (BFL) solutions do not perform well when data in a BFL cluster are sparse. A direct solution is to collect as many devices as possible to establish a large BFL cluster. However, these devices may locate in geographically distant areas and be separated by great distance, which further results in high communication latency. The high latency will lead to BFL's low system efficiency due to frequent communications in the blockchain consensus. In this article, we propose that the large cluster should be divided into multiple smaller clusters, each in its own geographical area and organized with a BFL. In this context, we propose CFL, a cross-cluster FL system facilitated by the cross-chain technique. CFL connects multiple BFL clusters, where only a few aggregated updates are transmitted over long distances across clusters, thus improving the system efficiency. The design of CFL focuses on a cross-chain consensus protocol, which guarantees the model updates to be exchanged securely across clusters. We carry out extensive experiments to evaluate CFL in comparison with BFL, and show both CFL's feasibility and efficiency.
【Keywords】Training; Hospitals; Mathematical model; Blockchain; Data models; Data privacy; Task analysis; Blockchain; cross-chain technology; federated learning (FL); Internet of Medical Things (IoMT)
【摘要】作为为医疗物联网 (IoMT) 提供隐私保护数据共享的一种方式，联邦学习 (FL) 越来越受欢迎。作为补充，区块链技术在最近的文献中被用来使 FL 安全。但是，当 BFL 集群中的数据稀疏时，现有的基于区块链的 FL (BFL) 解决方案表现不佳。一个直接的解决方案是收集尽可能多的设备来建立一个大型的 BFL 集群。然而，这些设备可能位于地理上遥远的区域并且相隔很远，这进一步导致高通信延迟。由于区块链共识中的频繁通信，高延迟将导致 BFL 的系统效率低下。在本文中，我们建议将大集群划分为多个较小的集群，每个集群都在自己的地理区域内，并由 BFL 组织。在这种情况下，我们提出了 CFL，这是一种由跨链技术促进的跨集群 FL 系统。 CFL 连接多个 BFL 集群，其中只有少数聚合更新跨集群长距离传输，从而提高了系统效率。 CFL 的设计侧重于跨链共识协议，保证模型更新在集群间安全交换。我们进行了广泛的实验来评估 CFL 与 BFL 的比较，并展示了 CFL 的可行性和效率。
【关键词】训练;医院；数学模型;区块链；数据模型；数据隐私；任务分析；区块链；跨链技术；联邦学习（FL）；医疗物联网 (IoMT)
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