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FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge
Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients.
Fast Server Learning Rate Tuning for Coded Federated Dropout
In cross-device Federated Learning (FL), clients with low computational power train a common\linebreak[4] machine model by exchanging parameters via updates instead of potentially private data.
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