Title: FedGMR: Federated Learning with Gradual Model Restoration under Asynchrony and Model Heterogeneity
Primary area: infrastructure, software libraries, hardware, systems, etc.
Keywords: Federated Learning, Heterogeneity, Model Pruning, Model-heterogeneous

Abstract: Federated learning (FL) holds strong potential for distributed machine learning. Yet in heterogeneous environments, Bandwidth-Constrained Clients (BCCs) often fail to participate effectively due to limited communication capacity, leading to slow convergence and degraded generalization.
To tackle this issue, we propose FedGMR—Federated Learning with Gradual Model Restoration under Asynchrony and Model Heterogeneity. FedGMR progressively increases each client's model capacity during training, enabling BCCs to contribute constantly throughout the training. In addition, a tailored transmission and aggregation mechanism is designed to better accommodate system-level heterogeneity. We establish convergence guarantees under mask-aware aggregation, showing that time-averaged, coverage-weighted densities govern enlarged errors, and that GMR provably tightens the gap to full-model FL. Extensive experiments on FEMNIST, CIFAR-10, and ImageNet-100 show that FedGMR achieves faster convergence and higher accuracy, especially under high heterogeneity and non-IID settings.