PGFed: Personalize Each Client’s Global Objective for Federated Learning-Reference-Cited by-同舟云学术

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PGFed: Personalize Each Client’s Global Objective for Federated Learning

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
Short-container-title:

Author:

Luo Jun¹,Mendieta Matias²,Chen Chen²,Wu Shandong¹

Affiliation:

1. University of Pittsburgh,Intelligent Systems Program,Pittsburgh,PA

2. University of Central Florida,Center for Research in Computer Vision,Orlando,FL

Funder

Health

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378181.pdf?arnumber=10378181

Reference44 articles.

1. Federated learning based on dynamic regularization;Emre Acar

2. Federated learning with personalization layers;Arivazhagan,2019

3. Federated meta-learning with fast convergence and efficient communication;Chen,2018

4. On bridging generic and personalized federated learning for image classification;Chen

5. Exploiting shared representations for personalized federated learning;Collins

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. SCFL: Spatio-temporal consistency federated learning for next POI recommendation;Information Processing & Management;2024-11

2. FedClust: Optimizing Federated Learning on Non-IID Data Through Weight-Driven Client Clustering;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

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