FedCoef: An Adaptive Aggregation Algorithm Based on Coefficient of Variation for Federated Learning

Federated learning has been successfully used in the Internet of Things (loTs) because it breaks data silos and protects data privacy and security by training shared global models through multi-client collaboration. However, the data distribution heterogeneity across loT devices will severely slow down the convergence of the global model and even result in a significant decrease in model accuracy. To address this issue, a federated adaptive aggregation algorithm was developed based on Coefficient of Variation. The proposed algorithm adaptively adjusts the aggregation weight factor of each local client model according to the difference in the coefficient of variation obtained before and after local model training. And the proportion of the current global model in the aggregation process is also dynamically adjusted in the middle and later stages. Extensive experiments demonstrate that the proposed method improves the accuracy of the global model and is also robust during the federated training process.