Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devices and edge devices. However, the centralized server architecture poses a risk as any challenge on the central/global server would result in the failure of the entire system. To minimize this risk, we propose a novel federated learning framework that leverages the deployment of multiple global servers. We posit that implementing multiple global servers in federated learning can enhance efficiency by capitalizing on local collaborations and aggregating knowledge, and the error tolerance in regard to communication failure in the single server framework would be handled. We therefore propose a novel framework that leverages the deployment of multiple global servers. We conducted a series of experiments using a dataset containing the event history of electric vehicle (EV) charging at numerous stations. We deployed a federated learning setup with multiple global servers and client servers, where each client-server strategically represented a different region and a global server was responsible for aggregating local updates from those devices. Our preliminary results of the global models demonstrate that the difference in performance attributed to multiple servers is less than 1%. While the hypothesis of enhanced model efficiency was not as expected, the rule for handling communication challenges added to the algorithm could resolve the error tolerance issue. Future research can focus on identifying specific uses for the deployment of multiple global servers.

翻译：联邦学习（FL）采用单一全局服务器框架是目前在去中心化环境（如移动设备和边缘设备）中训练机器学习模型的流行方法。然而，集中式服务器架构存在风险：中央/全局服务器一旦出现问题，将导致整个系统失效。为降低此风险，我们提出一种利用多全局服务器部署的新型联邦学习框架。我们假设，在联邦学习中引入多全局服务器可通过促进本地协作与知识聚合来提升效率，并能解决单服务器框架中通信故障的容错问题。因此，我们提出了一种基于多全局服务器部署的新框架。我们使用包含电动汽车（EV）充电站事件历史的数据集进行了一系列实验，搭建了包含多全局服务器与客户端服务器的联邦学习配置：每个客户端服务器策略性地代表不同区域，全局服务器负责聚合来自这些设备的本地更新。全局模型的初步结果表明，多服务器带来的性能差异小于1%。尽管模型效率提升的假设未完全达到预期，但算法中新增的通信故障处理规则可解决容错问题。未来研究可聚焦于多全局服务器部署的具体应用场景。