Client Selection in Federated Learning: A Dynamic Matching-Based Incentive Mechanism-Reference-Cited by-同舟云学术

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Client Selection in Federated Learning: A Dynamic Matching-Based Incentive Mechanism

Published:2024-02-19 Issue: Volume: Page:989-993
ISSN:
Container-title:2024 International Conference on Computing, Networking and Communications (ICNC)
language:
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Author:

Yellampalli Sai Sharanya¹,Chalupa Mikulas²,Wang Jingyi¹,Song Hyo Jung¹,Zhang Xinyue²,Yue Hao¹,Pan Miao³

Affiliation:

1. San Francisco State University,Department of Computer Science,San Francisco,CA,94132

2. Kennesaw State University,Department of Computer Science,Marietta,GA,30060

3. University of Houston,Department of Electrical and Computer Engineering,Houston,TX,77204

Funder

U.S. National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10555566/10555889/10556019.pdf?arnumber=10556019

Reference13 articles.

1. MLaaS: Machine Learning as a Service

2. Adaptive Privacy Preserving Deep Learning Algorithms for Medical Data

3. Communication-efficient learning of deep networks from decentralized data;McMahan

4. A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

5. Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

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