Delay and Overhead Efficient Transmission Scheduling for Federated Learning in UAV Swarms

TL;DR

This paper proposes a novel wireless scheduling framework for federated learning in UAV swarms, aiming to minimize delay and communication overhead in multi-hop UAV networks.

Contribution

It introduces an efficient transmission scheduling scheme that optimally aggregates and transmits model parameters in UAV-based federated learning systems.

Findings

Achieves minimal delay in model transmission

Reduces communication overhead significantly

Outperforms baseline scheduling methods

Abstract

This paper studies the wireless scheduling design to coordinate the transmissions of (local) model parameters of federated learning (FL) for a swarm of unmanned aerial vehicles (UAVs). The overall goal of the proposed design is to realize the FL training and aggregation processes with a central aggregator exploiting the sensory data collected by the UAVs but it considers the multi-hop wireless network formed by the UAVs. Such transmissions of model parameters over the UAV-based wireless network potentially cause large transmission delays and overhead. Our proposed framework smartly aggregates local model parameters trained by the UAVs while efficiently transmitting the underlying parameters to the central aggregator in each FL global round. We theoretically show that the proposed scheme achieves minimal delay and communication overhead. Extensive numerical experiments demonstrate the superiority of the proposed scheme compared to other baselines.