Federated learning is a distributed machine learning framework that enables multiple participants to train models collaboratively without sharing raw data. However, significant data transmission is required for parameter communication. As deep neural network models grow in size, deploying federated learning in complex network environments results in substantially increased communication costs. To address this challenge, we propose a pruning algorithm for deep federated text classification models, called FPrune. This algorithm evaluates the importance of locally trained models during the federated learning training stage by calculating the importance of each filter. Filters with lower importance are pruned. Additionally, we introduce a bidirectional pruning strategy that prunes filters on both the client and server sides. Experimental results demonstrate that the FPrune/25% and FPrune/50% algorithms reduce the communication cost by 70.22% and 42.03%, respectively, compared to FedAvg. Furthermore, the model's performance loss is limited to approximately 1.34%, demonstrating that the FPrune algorithm can effectively reduce communication costs while maintaining minimal performance degradation. The reduction in communication costs enables federated text classification models to scale more effectively within the federated learning framework, facilitating further applications of federated text classification, especially on resource limited mobile devices.
Li et al. (Thu,) studied this question.