Efficient Edge Intelligence under Clustering for UAV Swarm Networks

Unmanned aerial vehicles (UAVs) are expected to be widely used in many critical applications including monitoring, surveillance, urban target tracking, and delivery of goods, from military to civil fields. To realize that, empowering UAV swarm with edge intelligence is becoming appealing and inevitable. Federated learning (FL) as an emerging distributed collaborative machine learning (ML) without letting raw data out, could be suitable for UAV swarm networks but remains largely unexplored. In this paper, we first investigate the problem of efficient Edge Intelligence under Clustering for UAV swarm networks (e-EIC), i.e., how to jointly optimize the computation resource allocation and UAV clustering to minimize the total energy consumption of the UAV swarm for FL, subject to the training accuracy and latency constraints. We decompose the challenging e-EIC problem into two subproblems: computation resource allocation and UAV clustering, and accordingly propose an iterative algorithm based on the optimal policy of the first subproblem and local-search techniques for the second subproblem. Extensive performance evaluations demonstrate that our proposed algorithm can reduce more energy consumption of UAVs than three baseline algorithms, while satisfying the training accuracy and latency constraints.