Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning
This work addresses the challenge of deploying federated learning in infrastructure-poor environments, offering a practical solution for applications in remote areas, though it is incremental in extending FL to indirect communication scenarios.
The paper tackled the problem of enabling federated learning in remote areas without direct communication by proposing FedEx, a framework using UAVs as mobile transporters for indirect model exchange, which achieved consistent experimental results with theoretical analysis on two public datasets.
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.