Federated Learning for Healthcare InformaticsFederated learningHealthcarePrivacyWith the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among ...
Federated learning for healthcare informatics J. Healthc. Informat. Res., 5 (2021), pp. 1-19, 10.1007/s41666-020-00082-4 Google Scholar [13] Qiu P., Zhang X., Ji S., Du T., Pu Y., Zhou J., Wang T. Your labels are selling you out: Relation leaks in vertical federated learn...
Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nat. Commun. 11, 5131 (2020). Article CAS PubMed PubMed Central Google Scholar Rieke, N. et al. The future of digital health with federated learning. NPJ Dig. Med. 3, 119 (2020). Article...
Federated Learning for Healthcare Informatics Federated and Differentially Private Learning for Electronic Health Records A blockchain-orchestrated Federated Learning architecture for healthcare consortia Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data Stochastic Channel-Based Federated Learning...
Healthcare institutions and medical device manufacturers are under regulatory obligations to safeguard and protect the privacy of data they acquire from patients. This limits their ability to share the data with other institutions to collectively train machine learning models. Due to its ability in pres...
Federated Learning for Healthcare Informatics Federated and Differentially Private Learning for Electronic Health Records A blockchain-orchestrated Federated Learning architecture for healthcare consortia Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data Stochastic Channel-Based Federated Learning...
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitatio
Federated Learning (FL) is emerging as a transformative technology to tackle critical challenges in healthcare, particularly around data privacy, data heterogeneity, and scalability. Healthcare data, such as patient records and medical images, are exceptionally sensitive. FL allows for the use ...
Federated Learning for Medical Imaging - Intel AI Intel is partnering with the University of Pennsylvania and 19 other medical research institutions on development of a secure federated learning platform, which will enable collaborators to train a shared machine learning model for healthcare without exch...
Federated learning for healthcare informatics. J. Healthc. Inform. Res. 2020, 5, 1–19. [Google Scholar] [CrossRef] [PubMed] Pfitzner, B.; Steckhan, N.; Arnrich, B. Federated learning in a medical context: A systematic literature review. ACM Trans. Internet Technol. 2021, 21, 1–...