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FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task.
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution
Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important.
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