A Cost-Efficient Aggregation Strategy for Federated Learning in UAV Swarm Networks Under Non-IID Data

Federated learning has emerged as a promising approach for privacy-preserving model training across decentralized UAV swarm systems. However, challenges such as data heterogeneity, communication constraints, and limited computational resources significantly hinder convergence efficiency in real-world scenarios. This work introduces a communication-aware federated learning framework that integrates multi-dimensional cost modeling with dynamic client aggregation. The proposed cost function jointly considers communication overhead, computation latency, and training contribution. A Shapley-inspired client evaluation mechanism is incorporated to guide aggregation by prioritizing high-impact participants. In addition, a two-phase training strategy is devised to balance learning accuracy and resource efficiency across different training stages. Experimental results on the MNIST and CIFAR-10 benchmark datasets under non-IID settings demonstrate that the proposed method achieves faster convergence, higher accuracy, and reduced communication-computation cost. These results highlight its suitability for deployment in bandwidth-constrained, resource-limited UAV edge environments.