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Federated Learning (FL) is a distributed learning paradigm that scales on-device learning collaboratively and privately.
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, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, 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 Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 1912
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An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
L.M. Bregman · 1967
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Convex Analysis
R. Tyrrell Rockafellar · 1970
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Problem complexity and method efficiency in optimization
A.S. Nemirovski and D. B. Yudin · 1983
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Minimization Methods for Non-Differentiable Functions , volume 3
Naum Zuselevich Shor · 1985
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Legendre functions and the method of random Bregman projections
Heinz H Bauschke, Jonathan M Borwein, et al · 1997
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Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex
J. V. Haxby · 2001
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Fundamentals of Convex Analysis
Jean-Baptiste Hiriart-Urruty and Claude Lemaréchal · 2001
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Subgradient methods
Stephen Boyd, Lin Xiao, and Almir Mutapcic · 2003
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
I. Daubechies, M. Defrise, and C. De Mol · 2004
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Smooth minimization of non-smooth functions
Yu. Nesterov · 2005
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Exact Matrix Completion via Convex Optimization
Emmanuel J. Candès and Benjamin Recht · 2009
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Efficient online and batch learning using forward backward splitting
John Duchi and Yoram Singer · 2009
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Sparse online learning via truncated gradient
John Langford, Lihong Li, and Tong Zhang · 2009
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Efficient large-scale distributed training of conditional maximum entropy models
Ryan Mcdonald, Mehryar Mohri, Nathan Silberman, Dan Walker, and Gideon S. Mann · 2009
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Distributed Subgradient Methods for Multi-Agent Optimization
Angelia Nedic and Asuman Ozdaglar · 2009
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Primal-dual subgradient methods for convex problems
Yurii Nesterov · 2009
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A Singular Value Thresholding Algorithm for Matrix Completion
Jian-Feng Cai, Emmanuel J. Candès, and Zuowei Shen · 2010
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Composite objective mirror descent
John C. Duchi, Shai Shalev-shwartz, Yoram Singer, and Ambuj Tewari · 2010
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Sparse logistic regression for whole-brain classification of fMRI data
Srikanth Ryali, Kaustubh Supekar, Daniel A. Abrams, and Vinod Menon · 2010
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Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization
S. Sundhar Ram, A. Nedić, and V. V. Veeravalli · 2010
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Dual averaging methods for regularized stochastic learning and online optimization
Lin Xiao · 2010
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Parallelized stochastic gradient descent
Martin Zinkevich, Markus Weimer, Lihong Li, and Alex J. Smola · 2010
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Follow-the-regularized-leader and mirror descent: Equivalence theorems and L1 regularization
Brendan McMahan · 2011
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Optimal regularized dual averaging methods for stochastic optimization
Xi Chen, Qihang Lin, and Javier Pena · 2012
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Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
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Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
J. C. Duchi, A. Agarwal, and M. J. Wainwright · 2012
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Stochastic gradient descent with only one projection
Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, and Jinfeng Yi · 2012
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Distributed dual averaging for convex optimization under communication delays
K. I. Tsianos and M. 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
Cited alongside, same era.
Convergence of descent methods for semi-algebraic and tame problems: Proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods
Hedy Attouch, Jérôme Bolte, and Benar Fux Svaiter · 2013
Cited alongside, same era.
Revisiting Frank-Wolfe: Projection-free sparse convex optimization
Martin Jaggi · 2013
Cited alongside, same era.
Gradient methods for minimizing composite functions
Yu. Nesterov · 2013
Cited alongside, same era.
Machine learning for neuroimaging with scikit-learn
Alexandre Abraham, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gaël Varoquaux · 2014
Optimal distributed stochastic mirror descent for strongly convex optimization
Deming Yuan, Yiguang Hong, Daniel W.C. Ho, and Guoping Jiang · 2018
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On the convergence properties of a k-step averaging stochastic gradient descent algorithm for nonconvex optimization
Fan Zhou and Guojing Cong · 2018
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LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2019
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Federated Meta-Learning with Fast Convergence and Efficient Communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2019
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The Approximate Duality Gap Technique: A Unified Theory of First-Order Methods
Jelena Diakonikolas and Lorenzo Orecchia · 2019
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Cited alongside, same era.
Variable Metric Forward–Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function
Emilie Chouzenoux, Jean-Christophe Pesquet, and Audrey Repetti · 2014
Cited alongside, same era.
Proximal Algorithms , volume 1
Neal Parikh and Stephen P Boyd · 2014
Cited alongside, same era.
Distributed stochastic optimization and learning
Ohad Shamir and Nathan Srebro · 2014
Cited alongside, same era.
Minimization of Non-smooth, Non-convex Functionals by Iterative Thresholding
Kristian Bredies, Dirk A. Lorenz, and Stefan Reiterer · 2015
Cited alongside, same era.
Convex Optimization: Algorithms and Complexity
Sébastien Bubeck · 2015
Cited alongside, same era.
Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
Cited alongside, same era.
Florian Hartmann, Sunah Suh, Arkadiusz Komarzewski, Tim D. Smith, and Ilana Segall · 2019
Later among the works it cites.
Introducing TensorFlow Federated, 2019
Alex Ingerman and Krzys Ostrowski · 2019
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Improving Federated Learning Personalization via Model Agnostic Meta Learning
Yihan Jiang, Jakub Konečný, Keith Rush, and Sreeram Kannan · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Local SGD converges fast and communicates little
Sebastian U. Stich · 2019
Later among the works it cites.
On the computation and communication complexity of parallel SGD with dynamic batch sizes for stochastic non-convex optimization
Hao Yu and Rong Jin · 2019
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Adaptive Personalized Federated Learning
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman E. Ozdaglar · 2020
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On the rates of convergence of parallelized averaged stochastic gradient algorithms
Antoine Godichon-Baggioni and Sofiane Saadane · 2020
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Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
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Training keyword spotting models on non-iid data with federated learning
Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio Lopez-Moreno, and Rajiv Mathews · 2020
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh · 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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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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Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
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FedSplit: An algorithmic framework for fast federated optimization
Reese Pathak and Martin J. Wainwright · 2020
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Adaptive Federated Optimization
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and H. Brendan McMahan · 2020
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Personalized Federated Learning with Moreau Envelopes
Canh T. Dinh, Nguyen Tran, and Tuan Dung Nguyen · 2020
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Federated Nonconvex Sparse Learning
Qianqian Tong, Guannan Liang, Tan Zhu, and Jinbo Bi · 2020
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SlowMo: Improving communication-efficient distributed SGD with slow momentum
Jianyu Wang, Vinayak Tantia, Nicolas Ballas, and Michael Rabbat · 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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Distributed Mirror Descent for Online Composite Optimization
Deming Yuan, Yiguang Hong, Daniel W. C. Ho, and Shengyuan Xu · 2020
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Federated Accelerated Stochastic Gradient Descent
Honglin Yuan and Tengyu Ma · 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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Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, and Afshin Rostamizadeh · 2021
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