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，能够在数据保留在设备上的同时，为强大应用创建共享模型。这种方法具有提升数据隐私、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实现直接通信，例如在缺乏通信基础设施的偏远地区。为克服这一挑战，本文提出了一种名为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设备（如无人机）在服务器与客户端之间建立间接通信通道。这些运输设备作为中介，支持模型信息交换。间接通信的使用为收敛性分析与优化带来了新挑战，因为运输设备移动引入的延迟会对全局模型分发与局部模型收集造成问题。为此，本文提出了FedEx-Sync与FedEx-Async两种算法，分别支持运输设备层面的同步与异步学习。此外，还提出了一种双层优化算法，用于解决联合客户端分配与路径规划问题。使用两个公开数据集在模拟网络中进行实验验证，结果与理论一致，证明了FedEx的有效性。