arXiv:2607. 06653v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.
arXiv:2608. 13914v1 Announce Type: cross Abstract: Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training.
arXiv:2608. 02939v1 Announce Type: new Abstract: Electronic health record foundation models are limited by institutionally siloed data and substantial performance degradation under cross-site transfer.
arXiv:2508. 10017v2 Announce Type: replace-cross Abstract: Federated Learning (FL) presents a groundbreaking approach for collaborative health research, allowing model training on decentralized data while safeguarding patient privacy.
arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.