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Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Bursty and hierarchical structure in streams
Jon Kleinberg · 2003
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Finding frequent items in data streams
Moses Charikar, Kevin C. Chen, and Martin Farach-Colton · 2004
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An improved data stream summary: the count-min sketch and its applications
Graham Cormode and Shan Muthukrishnan · 2005
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Removing camera shake from a single photograph
Robert Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, and William T. Freeman · 2006
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Image and depth from a conventional camera with a coded aperture
Anat Levin, Robert Fergus, Frédo Durand, and William T. Freeman · 2007
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2008
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One sketch for all: Theory and application of conditional random sampling
Ping Li, Kenneth Ward Church, and Trevor Hastie · 2008
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Gradient distribution priors for biomedical image processing
Yuanhao Gong and Ivo F Sbalzarini · 2014
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Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Parallel sgd: When does averaging help?
Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré · 2016
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Qsgd: Communication-efficient sgd via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
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Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
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The convergence of sparsified gradient methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cédric Renggli · 2018
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SIGNSGD: compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and Bill Dally · 2018
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Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Total stochastic gradient algorithms and applications in reinforcement learning
Paavo Parmas · 2018
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On the convergence of federated optimization in heterogeneous networks
Privacy for free: Communication-efficient learning with differential privacy using sketches
Tian Li, Zaoxing Liu, Vyas Sekar, and Virginia Smith · 2019
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Variance reduced local sgd with lower communication complexity
Xianfeng Liang, Shuheng Shen, Jingchang Liu, Zhen Pan, Enhong Chen, and Yifei Cheng · 2019
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Enhancing the privacy of federated learning with sketching
Zaoxing Liu, Tian Li, Virginia Smith, and Vyas Sekar · 2019
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Sebastian U Stich and Sai Praneeth Karimireddy · 2019
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Local sgd converges fast and communicates little
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Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, and Virginia Smith · 2018
Cited alongside, same era.
Sparsified sgd with memory
Sebastian U Stich, Jean-Baptiste Cordonnier, and Martin Jaggi · 2018
Cited alongside, same era.
Communication compression for decentralized training
Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, and Ji Liu · 2018
Cited alongside, same era.
Jianyu Wang and Gauri Joshi · 2018
Cited alongside, same era.
Error compensated quantized sgd and its applications to large-scale distributed optimization
Jiaxiang Wu, Weidong Huang, Junzhou Huang, and Tong Zhang · 2018
Cited alongside, same era.
Fan Zhou and Guojing Cong · 2018
Cited alongside, same era.
Qsparse-local-sgd: Distributed SGD with quantization, sparsification and local computations
Debraj Basu, Deepesh Data, Can Karakus, and Suhas N. Diggavi · 2019
Cited alongside, same era.
Sebastian Urban Stich · 2019
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On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization
Hao Yu, Rong Jin, and Sen Yang · 2019
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Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning
Hao Yu, Sen Yang, and Shenghuo Zhu · 2019
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Toward communication efficient adaptive gradient method
Xiangyi Chen, Xiaoyun Li, and Ping Li · 2020
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Federated learning with compression: Unified analysis and sharp guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi · 2020
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A better alternative to error feedback for communication-efficient distributed learning
Samuel Horváth and Peter Richtárik · 2020
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Tighter theory for local SGD on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 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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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
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Don’t use large mini-batches, use local SGD
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, and Martin Jaggi · 2020
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Artemis: tight convergence guarantees for bidirectional compression in federated learning
Constantin Philippenko and Aymeric Dieuleveut · 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
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
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FetchSGD: Communication-efficient federated learning with sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
Closest in time.