FedGMR progressively restores sub-model capacity for bandwidth-constrained clients via gradual density increases and mask-aware aggregation, narrowing the gap to full-model federated learning.
Fiarse: Model- heterogeneous federated learning via importance-aware submodel extraction.arXiv preprint arXiv:2407.19389,
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Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
FedGMR progressively restores sub-model capacity for bandwidth-constrained clients via gradual density increases and mask-aware aggregation, narrowing the gap to full-model federated learning.