In the published article, there was an error in Article Type “[SYSTEMATIC REVIEW article]”, it should be “[ORIGINAL RESEARCH article]”.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Statements
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Summary
Keywords
COVID-19 detection, decentralized training, adaptive differential privacy, federated learning, convolutional neural network, healthcare data privacy
Citation
Ahmed R, Maddikunta PKR, Gadekallu TR, Alshammari NK and Hendaoui FA (2024) Corrigendum: Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images. Front. Med. 11:1504309. doi: 10.3389/fmed.2024.1504309
Received
30 September 2024
Accepted
03 October 2024
Published
24 October 2024
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
11 - 2024
Updates
Copyright
© 2024 Ahmed, Maddikunta, Gadekallu, Alshammari and Hendaoui.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Praveen Kumar Reddy Maddikunta praveenkumarreddy@vit.ac.in
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.