Friends to Help: Saving Federated Learning from Client Dropout-Reference-Cited by-同舟云学术

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Friends to Help: Saving Federated Learning from Client Dropout

Published:2024-04-14 Issue: Volume:34 Page:8896-8900
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Wang Heqiang¹,Xu Jie¹

Affiliation:

1. University of Miami,Electrical and Computer Engineering,Coral Gables,FL,USA,33146

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10447268.pdf?arnumber=10447268

Reference19 articles.

1. Scaffold: Stochastic controlled averaging for on-device federated learning;Karimireddy,2019

2. On the convergence of fedavg on non-iid data;Li

3. Achieving linear speedup with partial worker participation in non-iid federated learning;Yang

4. Communication-Efficient Federated Learning via Optimal Client Sampling

5. Optimal client sampling for federated learning;Chen;Transactions on Machine Learning Research,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive survey on client selection strategies in federated learning;Computer Networks;2024-09

2. Resource-Aware Split Federated Learning for Edge Intelligence;2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML);2024-05-13

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