Federated Learning (FL) is a machine learning approach that enables the creation of shared models for powerful applications while allowing data to remain on devices. This approach provides benefits such as improved data privacy, security, and reduced latency. However, in some systems, direct communication between clients and servers may not be possible, such as remote areas without proper communication infrastructure. To overcome this challenge, a new framework called FedEx (Federated Learning via Model Express Delivery) is proposed. This framework employs mobile transporters, such as UAVs, to establish indirect communication channels between the server and clients. These transporters act as intermediaries and allow for model information exchange. The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection. To address this, two algorithms, FedEx-Sync and FedEx-Async, are proposed for synchronized and asynchronized learning at the transporter level. Additionally, a bi-level optimization algorithm is proposed to solve the joint client assignment and route planning problem. Experimental validation using two public datasets in a simulated network demonstrates consistent results with the theory, proving the efficacy of FedEx.

翻译：联邦学习（FL）是一种机器学习方法，它允许在设备上保留数据的同时，为强大的应用创建共享模型。这种方法具有改善数据隐私、安全性以及降低延迟等优势。然而，在某些系统中，客户端与服务器之间可能无法直接通信，例如缺乏适当通信基础设施的偏远地区。为克服这一挑战，提出了一种名为FedEx（通过模型快递的联邦学习）的新框架。该框架利用移动运输器（如无人机）在服务器与客户端之间建立间接通信渠道。这些运输器作为中介，实现模型信息交换。间接通信的使用为收敛性分析与优化带来了新挑战，因为运输器移动引入的延迟会对全局模型分发和本地模型收集造成问题。为此，提出了两种算法——FedEx-Sync和FedEx-Async，分别用于在运输器层面实现同步与异步学习。此外，提出了一种双层优化算法，以解决联合客户端分配与路径规划问题。在模拟网络中使用两个公开数据集进行的实验验证结果与理论一致，证明了FedEx的有效性。