A Robust Aggregation Approach for Heterogeneous Federated Learning

Abstract

Federated learning is a cutting-edge method of model training, which leverages the end users to train the global model on the server. The end users are responsible for training locally on their datasets and update the shared global model. Once the local training is executed, the local trained models are forwarded back to the server to further upgrade the global model by performing aggregation. This process of global training is carried out for certain number of rounds. Practically, the datasets of clients are distributed heterogeneously. Thus, the updated local models by clients emanate broad variation among local models due to heterogeneity. In other words, the aggregation of local models plays a vital role in federated learning. Specifically, aggregating the diversified local models may deliver unsatisfactory output if not performed efficiently. This article presents a performance efficient and robust aggregation approach for heterogeneous federated learning called FedLbl. Our approach takes the diversity of data among clients into consideration before conducting the aggregation of local models. Our study compares the proposed method with conventional federated learning techniques, resulting in a 28% increase in accuracy and a 19% reduction in loss.