Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation-Reference-Cited by-同舟云学术

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Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation

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

Yang Fu-En¹,Wang Chien-Yi²,Wang Yu-Chiang Frank¹

Affiliation:

1. National Taiwan University

2. NVIDIA

Funder

National Science and Technology Council

National Taiwan University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377922.pdf?arnumber=10377922

Reference57 articles.

1. On the opportunities and risks of foundation models;Bommasani,2021

2. Language models are few-shot learners;Brown;NeurIPS,2020

3. Efficient personalized federated learning via sparse model-adaptation;Chen;ICML,2023

4. On bridging generic and personalized federated learning for image classification;Chen;ICLR,2022

5. On the importance and applicability of pre-training for federated learning;Chen;ICLR,2023

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. Personalized Federated Graph Learning on Non-IID Electronic Health Records;IEEE Transactions on Neural Networks and Learning Systems;2024-09

2. Personalized Federated Continual Learning via Multi-Granularity Prompt;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

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