Tackling data-heterogeneity variations in federated learning via adaptive aggregate weights

Abstract

In federated learning (FL), ensuring the efficiency of global models generated from the weighted aggregation of local models with data heterogeneity remains challenging. Moreover, the contradiction between imprecise aggregation weights and changing data distributions leads to aggregation errors that increase in an accelerated manner throughout the process. Therefore, we present federated learning using adaptive aggregate weights (FedAAW) to change the optimization direction in steps, including local training and global aggregation, and reduce inefficiencies in the global model due to the accelerated growth of aggregation errors resulting from changes in heterogeneity. In each round, the global- and local-model information is dynamically combined to generate an initial model at the beginning of the local training. The key module in FedAAW is adaptive aggregate weights (AAW), which are used to update the aggregation weight by sharing an optimization objective with global training and using the gradient information from other clients to accurately compute the updated aggregation weight direction. AAW guarantee consistency between weight update and global optimization, theoretically demonstrating convergence. The results of our comprehensive experiments on public datasets demonstrate that the test accuracy metrics of FedAAW are higher than those of six state-of-the-art algorithms and that FedAAW is capable of up to 50% improvement. FedAAW also results in an improvement of 14% on CIFAR100, a complex dataset, when compared with the best-performing baseline. FedAAW is faster than other algorithms in attaining the specified accuracy in experiments; in particular, it is approximately three times faster than federated learning with adaptive local aggregation. In addition, the results obtained in experimental environments with different client sizes and heterogeneous data confirm that FedAAW is robust and scalable.