Client Selection and Resource Allocation via Graph Neural Networks for Efficient Federated Learning in Healthcare Environments-Reference-Cited by-同舟云学术

Client Selection and Resource Allocation via Graph Neural Networks for Efficient Federated Learning in Healthcare Environments

Published:2024-06-26 Issue: Volume: Page:606-612
ISSN:
Container-title:Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments
language:
Short-container-title:

Author:

C. Messinis Sotiriοs¹^ORCID,Ε. Protonotarios Nicholas²^ORCID,Arapidis Emmanouil³^ORCID,Doulamis Nikolaos⁴^ORCID

Affiliation:

1. Institute of Communication and Computer Systems, Greece

2. Mathematics Research Center Academy of Athens, Greece

3. INFILI Technologies SA, Greece

4. School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Greece

Funder

HORIZON EUROPE Framework Programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652037.3663906

Reference22 articles.

1. REFL: Resource-Efficient Federated Learning

2. Cooperative Distributed Resource Allocation in Heterogeneous Networks With D2D Communication

3. Communication-Efficient Distributed Learning: An Overview

4. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

5. Zihan Chen, Jundong Li, and Cong Shen. 2023. Personalized Federated Learning with Attention-based Client Selection. arXiv preprint arXiv:2312.15148 (2023), 14 pages.

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1. Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks;Sensors;2024-08-08