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Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios.
When does a digraph admit a doubly stochastic adjacency matrix?
Bahman Gharesifard and Jorge Cortés · 2010
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Distributed stochastic subgradient projection algorithms for convex optimization
S Sundhar Ram, Angelia Nedić, and Venugopal V Veeravalli · 2010
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Dual averaging for distributed optimization: Convergence analysis and network scaling
John C Duchi, Alekh Agarwal, and Martin J Wainwright · 2011
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Online learning and online convex optimization
Shai Shalev-Shwartz et al · 2012
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Distributed dual averaging for convex optimization under communication delays
Konstantinos I Tsianos and Michael G Rabbat · 2012
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Push-sum distributed dual averaging for convex optimization
Konstantinos I Tsianos, Sean Lawlor, and Michael G Rabbat · 2012
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shai Shalev-Shwartz and Tong Zhang · 2013
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Trading computation for communication: Distributed stochastic dual coordinate ascent
Tianbao Yang · 2013
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Analysis of distributed stochastic dual coordinate ascent
Tianbao Yang, Shenghuo Zhu, Rong Jin, and Yuanqing Lin · 2013
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Communication-efficient distributed dual coordinate ascent
Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, and Michael I. Jordan · 2014
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Communication-efficient distributed online prediction by dynamic model synchronization
Michael Kamp, Mario Boley, Daniel Keren, Assaf Schuster, and Izchak Sharfman · 2014
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Distributed optimization over time-varying directed graphs
Angelia Nedić and Alex Olshevsky · 2014
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Federated Optimization:Distributed Optimization Beyond the Datacenter
Jakub Konečný, Brendan McMahan, and Daniel Ramage · 2015
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Adding vs. Averaging in Distributed Primal-Dual Optimization
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, and Martin Takáč · 2015
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Distributed optimization over time-varying directed graphs
Angelia Nedić and Alex Olshevsky · 2015
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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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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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Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Virginia Smith, Simone Forte, Chenxin Ma, Martin Takac, Michael I. Jordan, and Martin Jaggi · 2016
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Personalized and private peer-to-peer machine learning
Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi · 2018
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Federated Kernelized Multi-Task Learning
Sebastian Caldas, Virginia Smith, and Ameet Talwalkar · 2018
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Local SGD Converges Fast and Communicates Little
Sebastian U. Stich · 2018
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Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
Jianyu Wang and Gauri Joshi · 2018
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Hao Yu, Sen Yang, and Shenghuo Zhu · 2018
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Introduction to online convex optimization
Elad Hazan et al · 2016
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Coordinate dual averaging for decentralized online optimization with nonseparable global objectives
Soomin Lee, Angelia Nedić, and Maxim Raginsky · 2016
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Stochastic gradient-push for strongly convex functions on time-varying directed graphs
Angelia Nedić and Alex Olshevsky · 2016
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S Talwalkar · 2017
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Decentralized collaborative learning of personalized models over networks
Paul Vanhaesebrouck, Aurélien Bellet, and Marc Tommasi · 2017
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Privacy-preserving deep learning via additively homomorphic encryption
Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al · 2017
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Google AI Blog: Federated Learning: Collaborative Machine Learning without Centralized Training Data, April 2017
Brendan McMahan and Daniel Ramage · 2017
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S. Talwalkar · 2017
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Tao Lin, Sebastian U. Stich, and Martin Jaggi · 2018
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Cola: Decentralized linear learning
Lie He, An Bian, and Martin Jaggi · 2018
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Asynchronous decentralized parallel stochastic gradient descent
Xiangru Lian, Wei Zhang, Ce Zhang, and Ji Liu · 2018
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Towards more efficient stochastic decentralized learning: Faster convergence and sparse communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, and Hui Qian · 2018
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Pipe-sgd: A decentralized pipelined sgd framework for distributed deep net training
Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, and Alexander Schwing · 2018
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Milad Nasr, Reza Shokri, and Amir Houmansadr · 2018
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Mahmoud Assran and Michael Rabbat · 2018
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Stochastic gradient push for distributed deep learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, and Michael Rabbat · 2018
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
Closest in time.
Yawei Zhao, Chen Yu, Peilin Zhao, and Ji Liu · 2019
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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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