FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models-Reference-Cited by-同舟云学术

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FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models

Published:2024-05-17 Issue: Volume:33 Page:1544-1548
ISSN:
Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Cheng Yubin¹,Song Zhihuan¹,Jiang Xiaoyu¹,Yao Le²

Affiliation:

1. Zhejiang University,State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering,Hangzhou,China,310027

2. School of Mathematics, Hangzhou Normal University,Hangzhou,China

Funder

National Natural Science Foundation of China (NSFC)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606783.pdf?arnumber=10606783

Reference20 articles.

1. A Survey on Deep Learning for Data-Driven Soft Sensors

2. A Novel Feature-Extraction-Based Process Monitoring Method for Multimode Processes With Common Features and Its Applications to a Rolling Process

3. Feature Trend Extraction and Adaptive Density Peaks Search for Intelligent Fault Diagnosis of Machines

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

5. An image is worth 16x16 words: Transformers for image recognition at scale. arxiv 2020;Dosovitskiy;arXiv preprint,2010

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