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Personalized federated learning (FL) aims to train model(s) that can perform well for individual clients that are highly data and system heterogeneous.
Algorithm as 136: A k-means clustering algorithm
J. A. Hartigan and M. A. Wong · 1979
Earlier work this paper cites.
On the projected subgradient method for nonsmooth convex optimization in a hilbert space
Ya.I. Alber, A.N. Iusem, and M.V. Solodovz · 1998
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
Earlier work this paper cites.
Cifar-100 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
Earlier work this paper cites.
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan · 2009
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agøura y Arcas · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
Large scale distributed neural network training through online distillation
Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, and Geoffrey E. Hinton · 2018
Earlier work this paper cites.
International Workshop on Machine Learning on the Phone and other Consumer Devices in Conjunction with NeurIPS (NeurIPS-MLPCD)
Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim · 2018
Earlier work this paper cites.
Knowledge distillation by on-the-fly native ensemble
X. Lan, X. Zhu, and S. Gong · 2018
Earlier work this paper cites.
Deep mutual learning
Y. Zhang, T. Xiang, T. M. Hospedales, and H. Lu · 2018
Earlier work this paper cites.
Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurelien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adria Gascon, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecny, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Ozgur, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramer, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
Earlier work this paper cites.
Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
Earlier work this paper cites.
Parallel restarted SGD for non-convex optimization with faster convergence and less communication
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
Earlier work this paper cites.
Local SGD converges fast and communicates little
Sebastian U Stich · 2019
Earlier work this paper cites.
On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi · 2019
Earlier work this paper cites.
Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, , Nicholas B. Allen, Randy P. Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2019
Earlier work this paper cites.
Fedmd: Heterogenous federated learning via model distillation
Daliang Li and Junpu Wang · 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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Online learning, stability, and stochastic gradient descent, 2019
Tomaso Poggio, Stephen Voinea, and Lorenzo Rosasco · 2019
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Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
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Tighter theory for local SGD on identical and heterogeneous data
A Khaled, K Mishchenko, and P Richtárik · 2020
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A closer look at codistillation for distributed training
S. Sodhani, O. Delalleau, M. Assran, K. Sinha, N. Ballas, and M. Rabbat · 2020
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Distributed distillation for on-device learning
I. Bistritz, A. J. Mann, and N. Bambos · 2020
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Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U. Stich, and Martin Jaggi · 2020
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Personalized federated learning with moreau envelopes
Canh T. Dinh, Nguten H. Tran, and Tuan Dung Nguyen · 2020
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Adaptive distillation for decentralized learning from heterogeneous clients
Jiaxin Ma, Ryo Yonetani, and Zahid Iqbal · 2020
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Sebastian U Stich and Sai Praneeth Karimireddy · 2020
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Is local SGD better than minibatch SGD?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U Stich, Zhen Dai, Brian Bullins, H Brendan McMahan, Ohad Shamir, and Nathan Srebro · 2020
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A unified theory of decentralized SGD with changing topology and local updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian U Stich · 2020
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Faster on-device training using new federated momentum algorithm
Zhouyuan Huo, Qian Yang, Bin Gu, Lawrence Carin, and Heng Huang · 2020
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FedPD: A federated learning framework with optimal rates and adaptivity to non-IID data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 2020
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FedSplit: An algorithmic framework for fast federated optimization
Reese Pathak and Martin J Wainwright · 2020
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From local SGD to local fixed point methods for federated learning
Grigory Malinovsky, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtárik · 2020
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Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, and Koji Yamamoto · 2020
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Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S. Yu · 2020
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A field guide to federated optimization
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al · 2021
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Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms
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Adaptive federated optimization
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Personalized federated learning with first order model optimization
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Ditto: Fair and robust federated learning through personalization
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Federated model distillation with noise-free differential privacy
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Practical one-shot federated learning for cross-silo setting
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Distilled one-shot federated learning
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Edge bias in federated learning and its solution by buffered knowledge distillation
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Adversarial co-distillation learning for image recognition
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Federated Learning with Positive and Unlabeled Data
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