Abstract
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.