An Efficient and Accurate Dynamic Sparse Training Framework Based on Parameter-Freezing

Federated learning is a decentralized machine learning approach that consists of servers and clients. It protects data privacy during model training by keeping the training data locally in each client. However, the requirement for the server and clients to frequently synchronize the parameters of the model brings a heavy burden to the communication links, especially when the model size has grown drastically in recent years. Several methods have been proposed to compress the model size by sparsification to reduce the communication overhead, albeit with significant accuracy degradation. In this work, we propose methods to better trade-off between model accuracy and training efficiency in federated learning. Our first proposed method is a novel sparse mask readjustment rule on the server and the second is a parameter-freezing method during training on the clients. Experimental results show that the model accuracy has significantly improved when combining our proposed methods. For example, compared with the previous state-of-the-art methods with the same total amount of communication cost and computation FLOPs, the accuracy increases on average by 4% and 6% in our methods for CIFAR-10 and CIFAR-100 datasets on ResNet-18, respectively. On the other hand, when targeting the same accuracy, the proposed method can reduce the communication cost by 4-8 times for different datasets with different sparsity levels.