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With the increasing availability of Foundation Models, federated tuning has garnered attention in the field of federated learning, utilizing data and computation resources from multiple clients to collaboratively fine-tune foundation models.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Challenges and opportunities in edge computing
Blesson Varghese, Nan Wang, Sakil Barbhuiya, Peter Kilpatrick, and Dimitrios S Nikolopoulos · 2016
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, Brendan McMahan, et al · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Pytorch image models
Ross Wightman · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama · 2021
Cited alongside, same era.
Exploiting shared representations for personalized federated learning
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai · 2021
Cited alongside, same era.
Heterofl: Computation and communication efficient federated learning for heterogeneous clients
Enmao Diao, Jie Ding, and Vahid Tarokh · 2021
Cited alongside, same era.
Towards non-iid image classification: A dataset and baselines
Yue He, Zheyan Shen, and Peng Cui · 2021
Cited alongside, same era.
Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos Venieris, and Nicholas Lane · 2021
Cited alongside, same era.
Disentangled federated learning for tackling attributes skew via invariant aggregation and diversity transferring
Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, and Tieniu Tan · 2022
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Cross-domain federated adaptive prompt tuning for clip
Shangchao Su, Mingzhao Yang, Bin Li, and Xiangyang Xue · 2022
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Fate-llm: A industrial grade federated learning framework for large language models
Tao Fan, Yan Kang, Guoqiang Ma, Weijing Chen, Wenbin Wei, Lixin Fan, and Qiang Yang · 2023
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pfedprompt: Learning personalized prompt for vision-language models in federated learning
Tao Guo, Song Guo, and Junxiao Wang · 2023
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Scalefl: Resource-adaptive federated learning with heterogeneous clients
Fatih Ilhan, Gong Su, and Ling Liu · 2023
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Mlp-mixer: An all-mlp architecture for vision
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
Cited alongside, same era.
Tao Guo, Song Guo, Junxiao Wang, and Wenchao Xu · 2022
Cited alongside, same era.
Depthfl: Depthwise federated learning for heterogeneous clients
Minjae Kim, Sangyoon Yu, Suhyun Kim, and Soo-Mook Moon · 2022
Cited alongside, same era.
No one left behind: Inclusive federated learning over heterogeneous devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, and Xing Xie · 2022
Cited alongside, same era.
Learning federated visual prompt in null space for mri reconstruction
Chun-Mei Feng, Bangjun Li, Xinxing Xu, Yong Liu, Huazhu Fu, and Wangmeng Zuo
Cited in the paper.
Visual prompt based personalized federated learning
Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, and Dacheng Tao · 2023
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Adaptive channel sparsity for federated learning under system heterogeneity
Dongping Liao, Xitong Gao, Yiren Zhao, and Cheng-Zhong Xu · 2023
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Efficient federated prompt tuning for black-box large pre-trained models
Zihao Lin, Yan Sun, Yifan Shi, Xueqian Wang, Lifu Huang, Li Shen, and Dacheng Tao · 2023
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Fedclip: Fast generalization and personalization for clip in federated learning
Wang Lu, Xixu Hu, Jindong Wang, and Xing Xie · 2023
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Theoretical convergence guaranteed resource-adaptive federated learning with mixed heterogeneity
Yangyang Wang, Xiao Zhang, Mingyi Li, Tian Lan, Huashan Chen, Hui Xiong, Xiuzhen Cheng, and Dongxiao Yu · 2023
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Efficient model personalization in federated learning via client-specific prompt generation
Fu-En Yang, Chien-Yi Wang, and Yu-Chiang Frank Wang · 2023
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