Fetching the paper…
Reading the bibliography…
Federated learning uses a set of techniques to efficiently distribute the training of a machine learning algorithm across several devices, who own the training data.
Stochastic distributed learning with gradient quantization and variance reduction
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Sebastian Stich, and Peter Richtárik · 1904
Earlier work this paper cites.
Natural compression for distributed deep learning
Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, and Peter Richtárik · 1905
Earlier work this paper cites.
Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise
Jonathan C Mattingly, Andrew M Stuart, and Desmond J Higham · 2002
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2005
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
Earlier work this paper cites.
Ordinary differential equations: an introduction to nonlinear analysis , volume 13
Herbert Amann · 2011
Earlier work this paper cites.
Linear convergence of gradient and proximal-gradient methods under the Polyak-Lojasiewicz condition
Hamed Karimi, Julie Nutini, and Mark Schmidt · 2016
Earlier work this paper cites.
Federated learning: strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Qsgd: communication-efficient SGD via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Earlier work this paper cites.
Theoretical guarantees for approximate sampling from smooth and log-concave densities
Arnak S Dalalyan · 2017
Earlier work this paper cites.
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
Alain Durmus and Eric Moulines · 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.
Sarah: a novel method for machine learning problems using stochastic recursive gradient
Lam M Nguyen, Jie Liu, Katya Scheinberg, and Martin Takáč · 2017
Cited alongside, same era.
A family of functional inequalities: Łojasiewicz inequalities and displacement convex functions
Adrien Blanchet and Jérôme Bolte · 2018
Cited alongside, same era.
Sharp convergence rates for Langevin dynamics in the nonconvex setting
Xiang Cheng, Niladri S Chatterji, Yasin Abbasi-Yadkori, Peter L Bartlett, and Michael I Jordan · 2018
Cited alongside, same era.
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Andre Wibisono · 2018
Cited alongside, same era.
Nonasymptotic bounds for sampling algorithms without log-concavity
Mateusz B Majka, Aleksandar Mijatović, and Łukasz Szpruch · 2020
Later among the works it cites.
Analysis of Langevin Monte Carlo from Poincaré to log-Sobolev
Sinho Chewi, Murat A Erdogdu, Mufan Bill Li, Ruoqi Shen, and Matthew Zhang · 2021
Later among the works it cites.
On convergence of federated averaging Langevin dynamics
Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, and Guang Lin · 2021
Later among the works it cites.
Federated stochastic gradient Langevin dynamics
Khaoula El Mekkaoui, Diego Mesquita, Paul Blomstedt, and Samuel Kaski · 2021
Later among the works it cites.
Marina: faster non-convex distributed learning with compression
Eduard Gorbunov, Konstantin P Burlachenko, Zhize Li, and Peter Richtárik · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Analysis of Langevin Monte Carlo via convex optimization
Alain Durmus, Szymon Majewski, and Błażej Miasojedow · 2019
Cited alongside, same era.
Is there an analog of Nesterov acceleration for MCMC?
Yi-An Ma, Niladri Chatterji, Xiang Cheng, Nicolas Flammarion, Peter Bartlett, and Michael I Jordan · 2019
Cited alongside, same era.
Rapid convergence of the unadjusted Langevin algorithm: isoperimetry suffices
Santosh Vempala and Andre Wibisono · 2019
Cited alongside, same era.
Proximal Langevin algorithm: rapid convergence under isoperimetry
Andre Wibisono · 2019
Cited alongside, same era.
Faster non-convex federated learning via global and local momentum
Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S Dhillon, and Ufuk Topcu · 2020
Cited alongside, same era.
Scaffold: stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh · 2020
Cited alongside, same era.
Federated learning with compression: unified analysis and sharp guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi · 2021
Later among the works it cites.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Later among the works it cites.
Krishnakumar Balasubramanian, Sinho Chewi, Murat A Erdogdu, Adil Salim, and Matthew Zhang · 2022
Closest in time.
Flute: A scalable, extensible framework for high-performance federated learning simulations
Dimitrios Dimitriadis, Mirian Hipolito Garcia, Daniel Madrigal Diaz, Andre Manoel, and Robert Sim · 2022
Closest in time.
Qlsd: quantised Langevin stochastic dynamics for Bayesian federated learning
Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, and Eric Moulines · 2022
Closest in time.