Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning-Reference-Cited by-同舟云学术

Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning

Published:2024-08-24 Issue: Volume:34 Page:4191-4202
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Zhang Jiayuan¹^ORCID,Liu Xuefeng²^ORCID,Zhang Yukang³^ORCID,Zhu Guogang¹^ORCID,Niu Jianwei²^ORCID,Tang Shaojie⁴^ORCID

Affiliation:

1. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China

2. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University & Zhongguancun Laboratory, Beijing, China

3. Shenzhen International Graduate School, Tsinghua University, Beijing, China

4. Jindal School of Management, The University of Texas at Dallas, Richardson, TX, USA

Funder

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671908

Reference48 articles.

1. Wenxuan Bao, Tianxin Wei, Haohan Wang, and Jingrui He. 2023. Adaptive Test-Time Personalization for Federated Learning. In Thirty-seventh Conference on Neural Information Processing Systems.

2. Large-Scale Machine Learning with Stochastic Gradient Descent

3. Contrastive Test-Time Adaptation

4. Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein. 2021. RobustBench: a standardized adversarial robustness benchmark. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=SSKZPJCt7B

5. Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation