UAV-Aided Multi-Community Federated Learning

TL;DR

This paper proposes a UAV-assisted approach to optimize trajectory and scheduling in multi-community federated learning, improving training efficiency across diverse tasks with a novel heuristic and convex optimization.

Contribution

It introduces a heuristic metric and a joint optimization framework for UAV trajectory and device scheduling in multi-community federated learning.

Findings

Our method outperforms static and mobile UAV baselines.

The heuristic metric effectively proxies training performance.

Convex optimization enables efficient joint trajectory and scheduling design.

Abstract

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.