FedRAPS: Joint Client Selection and Adaptive Pruning for Resource-Aware Federated Learning

Federated learning (FL) enables collaborative training across distributed edge devices while preserving data privacy, but faces critical challenges in heterogeneous environments: diverse client computational capabilities, limited energy budgets, constrained communication bandwidth, and non-uniform data distributions. Existing approaches treat client selection and model adaptation independently or rely on static configurations, limiting adaptability and leading to suboptimal trade-offs between model accuracy and resource efficiency. To address these limitations, we propose FedRAPS (Federated Learning with Resource-Aware Pruning and Client Selection), a deep reinforcement learning-based framework for federated orchestration that jointly optimizes client selection and adaptive model pruning. We formulate federated orchestration as a reinforcement learning problem to balance model accuracy, energy consumption, and communication latency. The agent leverages per-client resource profiles and global training metrics to jointly select participating clients and assign client-specific pruning rates. Experiments on CIFAR-10 and Fashion-MNIST demonstrate that FedRAPS achieves 17– 21% energy savings and 8–21% training time reductions while maintaining similar accuracy compared to baseline FL methods.