Differentially-Private Federated Learning with Non-IID Data for Surgical Risk Prediction-Reference-Cited by-同舟云学术

Differentially-Private Federated Learning with Non-IID Data for Surgical Risk Prediction

Published:2024-02-05 Issue: Volume: Page:
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Container-title:2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)
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Author:

Pfitzner Bjarne¹,Maurer Max M.²,Winter Axel²,Riepe Christoph²,Sauer Igor M.²,Van de Water Robin¹,Arnrich Bert¹

Affiliation:

1. Hasso Plattner Institute,Digital Health - Connected Healthcare,Potsdam,Germany

2. Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin,Campus Charité Mitte | Campus Virchow Klinikum, Charité,Department of Surgery, Experimental Surgery,Berlin,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10504287/10504309/10504325.pdf?arnumber=10504325

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4. Defending against reconstruction attacks through differentially private federated learning for classification of heterogeneous chest x-ray data;Ziegler;Sensors,2022

5. Predictors of 30-Day Mortality Among Dutch Patients Undergoing Colorectal Cancer Surgery, 2011-2016

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