Self-Supervised On-Device Federated Learning From Unlabeled Streams-Reference-Cited by-同舟云学术

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Self-Supervised On-Device Federated Learning From Unlabeled Streams

Published:2023-12 Issue:12 Volume:42 Page:4871-4882
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Shi Jiahe¹^ORCID,Wu Yawen²,Zeng Dewen²,Tao Jun¹^ORCID,Hu Jingtong³^ORCID,Shi Yiyu²

Affiliation:

1. State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, China

2. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

3. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

https://ieeexplore.ieee.org/ielam/43/10325328/10128673-aam.pdf

Reference31 articles.

1. Collaborative Unsupervised Visual Representation Learning from Decentralized Data

2. Federated unsupervised representation learning;zhang;arXiv 2010 08982,2020

3. Divergence-aware federated self-supervised learning;zhuang;Proc Int Conf Learn Represent,2022

4. Decentralized Unsupervised Learning of Visual Representations

5. Active learning for convolutional neural networks: A core-set approach;sener;Proc Int Conf Learn Represent (ICLR),2018

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