BiFedKD improves ECG classification accuracy by 3.52% and Macro-F1 by 9.93% on MIT-BIH while cutting communication overhead 40% and computation cost 71.7% versus baseline federated methods.
Efficient federated learning on resource- constrained edge devices based on model pruning
2 Pith papers cite this work, alongside 21 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
AutoFLIP prunes federated models via one-time collective loss-landscape mapping and client-agreement-guided adaptation, reporting 52% lower computation and 65% lower communication with SOTA non-IID accuracy.
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BiFedKD: Bidirectional Federated Knowledge Distillation Framework for Non-IID and Long-Tailed ECG Monitoring
BiFedKD improves ECG classification accuracy by 3.52% and Macro-F1 by 9.93% on MIT-BIH while cutting communication overhead 40% and computation cost 71.7% versus baseline federated methods.
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Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
AutoFLIP prunes federated models via one-time collective loss-landscape mapping and client-agreement-guided adaptation, reporting 52% lower computation and 65% lower communication with SOTA non-IID accuracy.