Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
Asyncfeded: Asynchronous federated learning with euclidean distance based adaptive weight aggregation.arXiv preprint arXiv:2205.13797
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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.
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Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
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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.