Learning Client Selection Strategy for Federated Learning across Heterogeneous Mobile Devices-Reference-Cited by-同舟云学术

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Learning Client Selection Strategy for Federated Learning across Heterogeneous Mobile Devices

Published:2024-04-03 Issue: Volume:2 Page:1-7
ISSN:
Container-title:2024 25th International Symposium on Quality Electronic Design (ISQED)
language:
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Author:

Zhang Sai Qian¹,Lin Jieyu²,Zhang Qi³,Chen Yu-Jia⁴

Affiliation:

1. New York University,Department of Electrical Engineering and Computer Science,New York,USA

2. University of Toronto,Department of Electrical and Computer Engineering,Toronto,Canada

3. Microsoft Corporation,Redmond,WA

4. National Central University,Department of Communication Engineering,Taoyuan,Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10528356/10528364/10528721.pdf?arnumber=10528721

Reference16 articles.

1. Communication-efficient learning of deep networks from decentralized data;McMahan;Artificial intelligence and statistics,2017

2. Federated learning with non-iid data;Zhao;arXiv preprint arXiv:1806.00582,2018

3. Scaffold: Stochastic controlled averaging for federated learning;Karimireddy;arXiv preprint arXiv:1910.06378,2019

4. Federated optimization in heterogeneous networks;Li;Proceedings of Machine Learning and Systems,2020

5. SphereFed: Hyperspherical Federated Learning

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