Optimizing Efficiency through Adaptive Model Pruning in Fully Distributed Federated Learning

Federated learning (FL) enables effective model training across distributed devices while protecting data privacy. However, high communication costs challenge practical FL, particularly with computing-capability-limited local devices. To address this, we propose a novel FL strategy in device-to-device (D2D) networks with model pruning. Dynamic clustering using k-means algorithm enhances information exchange efficiency, while model pruning reduces local model complexity and training latency. We derive a convergence bound of $\mathcal{O}\left( {{t^{ - 1}}} \right)$ and optimize the pruning ratio and bandwidth allocation using KKT conditions. Simulation results reveal that without the sacrifice of testing accuracy, the proposed FL algorithm requires only 88% of the training time compared to the system employing equal pruning, and 80% of the training time compared to the system without pruning.