Transforming Healthcare with Federated Learning-based Artficial Intelligence: Concepts, Classifications, and Challenges-Reference-Cited by-同舟云学术

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Transforming Healthcare with Federated Learning-based Artficial Intelligence: Concepts, Classifications, and Challenges

Published:2024-03-13 Issue: Volume:5 Page:209-216
ISSN:
Container-title:2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL)
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Author:

Maheswari G. Uma¹,Jeslin J. Gnana¹,Rajasuguna M.²,S Amutha³

Affiliation:

1. RMK College of Engineering and Technology,Chennai,Tamil Nadu,India

2. RMK College of Engineering and Technology,Department of Computer Science and Engineering,Chennai,Tamil Nadu

3. R&D Institute of Science and Technology,Vel Tech Rangarajan Dr.Sagunthala,Chennai,Tamil Nadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10601356/10601357/10601394.pdf?arnumber=10601394

Reference10 articles.

1. Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout

2. SecureBoost: A Lossless Federated LearningFramework;Cheng;arXiv preprint,2021

3. GDPR. 2018. General Data Protection Regulation. Accessed: Decem-ber 8, 2021.https://gdpr-info.eu/Ghorbani, A., and J. Zou. 2019. “Data Shapley: Equitable Val-uation of Data for Machine Learning

4. The Beta Reputation System;Josang

5. Advances and Open Problems in Federated Learning

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