Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recently. Despite the different forms of FL algorithms (e.g., synchronous FL, asynchronous FL) and the underlying optimization methods, nearly all existing works implicitly assumed the existence of a communication infrastructure that facilitates the direct communication between the server and the clients for the model data exchange. This assumption, however, does not hold in many real-world applications that can benefit from distributed learning but lack a proper communication infrastructure (e.g., smart sensing in remote areas). In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e.g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients. Two algorithms, called FedEx-Sync and FedEx-Async, are developed depending on whether the transporters adopt a synchronized or an asynchronized schedule. Even though the indirect communications introduce heterogeneous delays to clients for both the global model dissemination and the local model collection, we prove the convergence of both versions of FedEx. The convergence analysis subsequently sheds lights on how to assign clients to different transporters and design the routes among the clients. The performance of FedEx is evaluated through experiments in a simulated network on two public datasets.

翻译：联邦学习（FL）是一种通信高效且保护隐私的分布式机器学习框架，近年来引起了大量研究关注。尽管FL算法存在不同形式（如同步FL、异步FL）以及底层优化方法的差异，几乎所有现有工作都隐含假设存在一种通信基础设施，能够支持服务器与客户端之间直接进行模型数据交换。然而，这一假设在诸多可从分布式学习中受益但缺乏适当通信基础设施的实际应用场景中（例如偏远地区的智能感知）并不成立。本文提出了一种新颖的FL框架，名为FedEx（即通过模型快递实现联邦学习的简称），该框架利用移动运输工具（例如无人机）在服务器与客户端之间建立间接通信渠道。我们开发了两种算法，分别称为FedEx-Sync和FedEx-Async，其区别在于运输工具采用同步调度还是异步调度。尽管间接通信会为客户端在全局模型分发和本地模型收集过程中引入异构延迟，但我们证明了两种版本FedEx的收敛性。随后的收敛分析揭示了如何将客户端分配给不同的运输工具以及如何在客户端之间设计路线。通过在模拟网络上使用两个公共数据集进行实验，评估了FedEx的性能。