Two-way Delayed Updates with Model Similarity in Communication-Efficient Federated Learning

The great achievement of IoT and the wide use of edge devices have brought explosive growth in data. The quality and scale of data determine the performances of machine learning models. Federated learning has attracted widespread attention for its ability to use isolated data and protect data privacy. Models can represent excellent generalization capabilities through federated training. However, the large number of devices and complex models involved in federated training exacerbate the communication costs and degrade the performance of the global model. Although existing approaches can reduce communication costs, they ignore the degradation of global model accuracy in a heterogeneous environment. To alleviate the huge communication costs in federated learning, this paper focuses on reducing upstream and downstream communication frequency while ensuring global model accuracy. We propose a Two-way Delayed Updates method with Model Similarity in Communication-Efficient Federated Learning (FedTDMS). FedTDMS employs personalized local computation to improve global model accuracy on heterogeneous data. Combining local update relevance check and global model compensation, FedTDMS reduces the communication frequency in Federated Learning. We conduct experiments on the MNIST-FL and CIFAR-10-FL datasets. Results show that FedTDMS can greatly optimize communication efficiency while maintaining good global model accuracy.