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.

提出了一种名为 FedEx 的框架，该框架使用移动运输工具（如无人机）来建立服务器和客户端之间的间接通信通道，实现了联邦学习的远程访问，并提出了两种算法 FedEx-Sync 和 FedEx-Async，以解决运输工具级别上的同步和异步学习，以及双层优化算法，以解决客户分配和路径规划问题。制定实验验证 FedEx 的一致结果证明其有效性。

联邦学习间接通信中的客户端分配和无人机路径规划

Joint Client Assignment and UAV Route Planning for  Indirect-Communication Federated Learning

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.

本文提出了一种基于移动传输器的联邦学习框架 FedEx (FL via Model Express Delivery)，旨在解决缺乏适当通信基础设施的实际应用中数据共享的问题。FedEx-Sync 和 FedEx-Async 两种算法分别采用同步和异步调度策略，并通过收敛性分析指导客户端分配和传输路线设计。在两个公共数据集的模拟网络上，我们通过实验证明了 FedEx 的性能。