Personalized Federated Learning with Auction-Based Client Selection and Edge-Enhanced Model Accuracy-Reference-Cited by-同舟云学术

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Personalized Federated Learning with Auction-Based Client Selection and Edge-Enhanced Model Accuracy

Published:2024-06-30 Issue: Volume:abs/2307.09478 Page:1-10
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
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Author:

Mo Kaiwei¹,Li Xianglong²,Lin Wei³,Xu Hong⁴,Li Zongpeng⁵,Xue Chun Jason⁶

Affiliation:

1. City University of Hong Kong,Computer Science

2. Wuhan University,Computer Science

3. The Chinese University of Hong Kong,Computer Science

4. The Chinese University of Hong Kong,Computer Science & Engineering

5. Tsinghua University,Network Sciences & Cyberspace

6. MBZUAI,Computer Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650279.pdf?arnumber=10650279

Reference35 articles.

1. Communication-efficient learning of deep networks from decentralized data;Brendan,2017

2. Federated learning with non-iid data;Zhao,2018

3. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data;Jeong,2018

4. Adaptive personalized federated learning;Deng,2020

5. Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

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