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Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-the-cloud setting to distributed training over edge devices in order to strengthen data privacy.
Signature verification using a “siamese” time delay neural network
Jane Bromley, James W Bentz, Léon Bottou, Isabelle Guyon, Yann LeCun, Cliff Moore, Eduard Säckinger, and Roopak Shah · 1993
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Improving semi-supervised federated learning by reducing the gradient diversity of models
Zhengming Zhang, Yaoqing Yang, Zhewei Yao, Yujun Yan, Joseph E Gonzalez, and Michael W Mahoney · 2008
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Federated unsupervised representation learning
Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, and Xiaolin Li · 2010
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2012
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Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Sinkhorn distances: Lightspeed computation of optimal transportation distances, 2013
Marco Cuturi · 2013
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P. Kingma · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 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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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Unsupervised feature learning via non-parametric instance-level discrimination, 2018
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin · 2018
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Virtual adversarial training: A regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-Ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Federated self-supervised learning of multisensor representations for embedded intelligence
Aaqib Saeed, Flora D Salim, Tanir Ozcelebi, and Johan Lukkien · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2020
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Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
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Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
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Adaptive federated optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečnỳ, Sanjiv Kumar, and H Brendan McMahan · 2020
Cited alongside, same era.
Rc-ssfl: Towards robust and communication-efficient semi-supervised federated learning system
Yi Liu, Xingliang Yuan, Ruihui Zhao, Yifeng Zheng, and Yefeng Zheng · 2020
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Fedsemi: An adaptive federated semi-supervised learning framework
Zewei Long, Liwei Che, Yaqing Wang, Muchao Ye, Junyu Luo, Jinze Wu, Houping Xiao, and Fenglong Ma · 2020
Cited alongside, same era.
Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, and Koji Yamamoto · 2020
Cited alongside, same era.
Federated semi-supervised learning with inter-client consistency
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang · 2020
Cited alongside, same era.
Semi-supervised federated learning for activity recognition
Yuchen Zhao, Hanyang Liu, Honglin Li, Payam Barnaghi, and Hamed Haddadi · 2020
Cited alongside, same era.
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr · 2020
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Tinytl: Reduce memory, not parameters for efficient on-device learning
Han Cai, Chuang Gan, Ligeng Zhu, and Song Han · 2020
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Advances and open problems in federated learning
P. Kairouz, H. B. McMahan, B. Avent, Aurélien Bellet, M. Bennis, A. Bhagoji, Keith Bonawitz, Zachary B. Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, S. Rouayheb, David Evans, Josh Gardner, Zachary Garrett, A. Gascón, Badih Ghazi, Phillip B. Gibbons, M. Gruteser, Z. Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, T. Javidi, Gauri Joshi, M. Khodak, Jakub Konecný, Aleksandra Korolova, F. Koushanfar, O. Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, M. Mohri, R. Nock, A. Özgür, R. Pagh, Mariana Raykova, Hang Qi, D. Ramage, R. Raskar, D. Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, A. T. Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, F. Yu, Han Yu, and Sen Zhao · 2021
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A field guide to federated optimization
Jianyu Wang, Zachary B. Charles, Zheng Xu, Gauri Joshi, H. B. McMahan, B. A. Y. Arcas, Maruan Al-Shedivat, Galen Andrew, S. Avestimehr, Katharine Daly, Deepesh Data, S. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, A. Ingerman, Martin Jaggi, T. Javidi, P. Kairouz, Satyen Kale, Sai Praneeth Reddy Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, M. Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, K. Singhal, Virginia Smith, M. Soltanolkotabi, Weikang Song, A. T. Suresh, Sebastian U. Stich, Ameet S. Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, M. Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, and Wennan Zhu · 2021
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Self-supervised cross-silo federated neural architecture search
Xinle Liang, Yang Liu, Jiahuan Luo, Yuanqin He, Tianjian Chen, and Qiang Yang · 2021
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Unsupervised learning of visual features by contrasting cluster assignments, 2021
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2021
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