Promoting Large Margins for Robust and Efficient Federated Learning

We introduce FedMargin, a novel Federated Learning (FL) framework designed to enhance the performance of state-of-the-art algorithms by incorporating a large-margin promoting term into the client's loss function. By leveraging large margin - a well-established robustness measure in traditional Machine Learning - we prove that the parameter trajectories of clients are less prone to drift, leading to faster convergence and improved classification accuracy. This effect is particularly pronounced in non-i.i.d. scenarios. \method is designed to be seamlessly integrable with minimal computational and implementation overhead. An extensive evaluation on four benchmark datasets demonstrates that FedMargin consistently improves both the performance and convergence of the considered FL baseline algorithms.