Communication-Efficient Federated Swarm Learning for SAG-CM on Heterogeneous Data

Federated learning (FL) enables multiple clients to collaboratively trainmodels without sharing private data, but it often struggles under non-IID distributions and tight edge budgets- particularly in systems, such as space-air-ground collaborative monitoring (SAG-CM), where satellite, aerial, and ground sensors generate heterogeneous, large-scale data across constrained networks. Inspired by the biological intelligence of gregarious organisms, we propose multistrategy grey wolf optimization-federated swarm learning (MSGWO-FSL), a novel edge-learning framework for SAG-CM/federated systems. Our design integrates artificial intelligence-enabled stochastic gradient descent with bioinspired multistrategy grey wolf optimization: each client maintains a local (α, β, δ) leader-set to explore both weights and sparsity, and uploads only the binary mask of the α model. The server aggregates the masks to preserve personalization by consolidating common structure while retaining client-specific sparsity, yielding sparse, faster on-device models with reduced communication overhead. Our convergence analysis theoretically demonstrates that MSGWO-FSL outperforms standard FL under mild conditions, and we derive a model-divergence upper bound that guides stable step-size choices under non-IID data. Extensive experiments on five public benchmarks and two public remote-sensing benchmarks demonstrate MSGWO-FSL surpasses nine state-of-the-art baselines in average accuracy while maintaining communication and computation efficiency.