Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals
29
Zitationen
17
Autoren
2022
Jahr
Abstract
FedAvg can significantly improve the generalization of the model compared to other personalization FL algorithms; however, at the cost of poor internal validity. Personalized FL may offer an opportunity to develop both internal and externally validated algorithms.
Ähnliche Arbeiten
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
2002 · 8.402 Zit.
Calibrating Noise to Sensitivity in Private Data Analysis
2006 · 6.888 Zit.
Deep Learning with Differential Privacy
2016 · 5.614 Zit.
Communication-Efficient Learning of Deep Networks from Decentralized\n Data
2016 · 5.593 Zit.
Large-Scale Machine Learning with Stochastic Gradient Descent
2010 · 5.572 Zit.
Autoren
Institutionen
- University of Minnesota(US)
- Emory University(US)
- Fairview Health Services(US)
- Nvidia (United States)(US)
- Indiana University – Purdue University Indianapolis(US)
- University of Florida Health(US)
- Florida College(US)
- University of Florida(US)
- Indiana University School of Medicine
- University of Minnesota System(US)