Abstract
Federated learning (FL) has emerged as a machine learning (ML) paradigm
for distributed training without requiring trainers to share private
data. Coevally, unmanned aerial vehicles (UAVs) become sufficiently
powerful that they can contribute to training sophisticated ML models.
Direct application of the conventional FL framework to a UAV swarm,
however, could result in unnecessarily high communication and
computation complexity. This paper aims to address this challenge by
proposing a scalable and clustered FL framework for such large UAV
swarms. Specifically, we develop a clustering scheme based on which the
UAV-based wireless network is partitioned into different clusters
coordinated by corresponding cluster-head UAVs forming a connected
graph. While the cluster-head UAVs coordinate the ML model updates as in
the conventional FL framework, we propose two intercluster model
aggregation strategies to produce the final global model in each
training round. Extensive numerical studies demonstrate the convergence
and desirable trade-offs between training performance and communication
efficiency of our proposed framework.