Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions-Reference-Cited by-同舟云学术

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Published:2023 Issue: Volume: Page:1-1
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Rauniyar Ashish¹^ORCID,Hagos Desta Haileselassie²,Jha Debesh³^ORCID,Håkegård Jan Erik¹,Bagci Ulas³,Rawat Danda B.²^ORCID,Vlassov Vladimir⁴^ORCID

Affiliation:

1. Sustainable Communication Technologies (SCT), SINTEF Digital, Trondheim, Norway

2. Department of Electrical Engineering and Computer Science, DoD Center of Excellence in Artificial Intelligence and Machine Learning (CoE-AIML), College of Engineering and Architecture (CEA), Howard University, Washington, DC, USA

3. Machine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, USA

4. Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

Funder

Norges Forskningsr?d

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/6702522/10304218.pdf?arnumber=10304218

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