Fetching the paper…
Reading the bibliography…
Federated Learning (FL) coordinates with numerous heterogeneous devices to collaboratively train a shared model while preserving user privacy.
Problem complexity and method efficiency in optimization
Arkadij Semenovič Nemirovskij and David Borisovich Yudin · 1983
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Information-theoretic lower bounds on the oracle complexity of convex optimization
Alekh Agarwal, Martin J Wainwright, Peter Bartlett, and Pradeep Ravikumar · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Distributed delayed stochastic optimization
Alekh Agarwal and John C Duchi · 2012
Earlier work this paper cites.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
Saeed Ghadimi and Guanghui Lan · 2012
Earlier work this paper cites.
Bandits with heavy tail
Sébastien Bubeck, Nicolo Cesa-Bianchi, and Gábor Lugosi · 2013
Earlier work this paper cites.
Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
Earlier work this paper cites.
Lenet-5, convolutional neural networks
Yann LeCun et al · 2015
Earlier work this paper cites.
Asynchronous parallel stochastic gradient for nonconvex optimization
Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu · 2015
Earlier work this paper cites.
Analysis and implementation of an asynchronous optimization algorithm for the parameter server
Arda Aytekin, Hamid Reza Feyzmahdavian, and Mikael Johansson · 2016
Earlier work this paper cites.
An asynchronous mini-batch algorithm for regularized stochastic optimization
Hamid Reza Feyzmahdavian, Arda Aytekin, and Mikael Johansson · 2016
Earlier work this paper cites.
Federated optimization: Distributed machine learning for on-device intelligence
Jakub Konečnỳ, H Brendan McMahan, Daniel Ramage, and Peter Richtárik · 2016
Earlier work this paper cites.
How to escape saddle points efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M Kakade, and Michael I Jordan · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Cited alongside, same era.
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
Cited alongside, same era.
Accelerated methods for nonconvex optimization
Yair Carmon, John C Duchi, Oliver Hinder, and Aaron Sidford · 2018
Cited alongside, same era.
High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
Cited alongside, same era.
Lower bounds for non-convex stochastic optimization, 2019
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, and Blake Woodworth · 2019
Cited alongside, same era.
Qsparse-local-sgd: Distributed sgd with quantization, sparsification, and local computations
A tight convergence analysis for stochastic gradient descent with delayed updates
Yossi Arjevani, Ohad Shamir, and Nathan Srebro · 2020
Later among the works it cites.
Asynchronous distributed optimization with stochastic delays
Margalit Glasgow and Mary Wootters · 2020
Later among the works it cites.
Scaffold: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Later among the works it cites.
Tighter theory for local sgd on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
Later among the works it cites.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Debraj Basu, Deepesh Data, Can Karakus, and Suhas Diggavi · 2019
Cited alongside, same era.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 2019
Cited alongside, same era.
Semi-cyclic stochastic gradient descent
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, and Kunal Talwar · 2019
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
Cited alongside, same era.
Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
Cited alongside, same era.
Local sgd converges fast and communicates little
Sebastian Urban Stich · 2019
Cited alongside, same era.
Cooperative sgd: A unified framework for the design and analysis of communication-efficient sgd algorithms
Jianyu Wang and Gauri Joshi · 2019
Cited alongside, same era.
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2020
Later among the works it cites.
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, and Ramtin Pedarsani · 2020
Later among the works it cites.
The error-feedback framework: Better rates for sgd with delayed gradients and compressed updates
Sebastian U Stich and Sai Praneeth Karimireddy · 2020
Later among the works it cites.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor · 2020
Later among the works it cites.
Distributed non-convex optimization with sublinear speedup under intermittent client availability
Yikai Yan, Chaoyue Niu, Yucheng Ding, Zhenzhe Zheng, Fan Wu, Guihai Chen, Shaojie Tang, and Zhihua Wu · 2020
Later among the works it cites.
Why are adaptive methods good for attention models?, 2020
Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J Reddi, Sanjiv Kumar, and Suvrit Sra · 2020
Later among the works it cites.
Taming convergence for asynchronous stochastic gradient descent with unbounded delay in non-convex learning
Xin Zhang, Jia Liu, and Zhengyuan Zhu · 2020
Later among the works it cites.
Towards flexible device participation in federated learning
Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, and Carlee Joe-Wong · 2021
Closest in time.
Achieving linear speedup with partial worker participation in non-iid federated learning
Haibo Yang, Minghong Fang, and Jia Liu · 2021
Closest in time.