Progressive Federated Learning Scheme Based on Model Pruning

Federated learning is an effective distributed learning framework that protects privacy and allows multiple edge devices to work together to train models jointly without exchanging data. However, edge devices usually have limited computing capabilities, and limited network bandwidth is often a major bottleneck. In order to reduce communication and computing costs, we introduced a horizontal pruning mechanism, combined federated learning and progressive learning, and proposed a progressive federated learning scheme based on model pruning. It gradually trains from simple models to more complex ones and trims the uploaded models horizontally. Our approach effectively reduces computational and bidirectional communication costs while maintaining model performance. Several image classification experiments on different models have been conducted by us, and the experimental results demonstrate that our approach can effectively save approximately 10% of the computational cost and 48% of the communication cost when compared to FedAvg.