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This paper proposes FedDualAtt, a personalized federated learning approach for ECG classification that splits transformer attention heads into globally aggregated and locally private branches to handle data heterogeneity across clinical sites. Experiments on the FedCVD benchmark show improved performance over existing methods.
Researchers used AlphaFold and cryo-EM to map the structure of the apoB100 protein, which forms bad cholesterol, marking a significant breakthrough in understanding heart disease.