# A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning

@inproceedings{Mishchenko2018ADP, title={A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning}, author={Konstantin Mishchenko and Franck Iutzeler and J{\'e}r{\^o}me Malick and Massih-Reza Amini}, booktitle={ICML}, year={2018} }

Distributed learning aims at computing high-quality models by training over scattered data. This covers a diversity of scenarios, including computer clusters or mobile agents. One of the main challenges is then to deal with heterogeneous machines and unreliable communications. In this setting, we propose and analyze a flexible asynchronous optimization algorithm for solving nonsmooth learning problems. Unlike most existing methods, our algorithm is adjustable to various levels of communication…

## 26 Citations

A Distributed Flexible Delay-Tolerant Proximal Gradient Algorithm

- Computer ScienceSIAM J. Optim.
- 2020

This work develops and analyzes an asynchronous algorithm for distributed convex optimization when the objective writes a sum of smooth functions, local to each worker, and a non-smooth function, and proves that the algorithm converges linearly in the strongly convex case, and provides guarantees of convergence for the non-strongly conveX case.

Delay-adaptive step-sizes for asynchronous learning

- Computer ScienceICML
- 2022

This paper develops general convergence results for delay-adaptive asynchronous iterations and specialize these to proximal incremental gradient descent and block-coordinate descent algorithms and demonstrates how delays can be measured on-line, present delay- Adaptive step-size policies, and illustrate their theoretical and practical advantages over the state-of-the-art.

Optimal convergence rates of totally asynchronous optimization

- Computer Science
- 2022

This paper derives explicit convergence rates for the proximal incremental aggregated gradient (PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under a specific model of total asynchrony, and shows that the derived rates are order-optimal.

Asynchronous Distributed Learning with Sparse Communications and Identification

- Computer ScienceArXiv
- 2018

An asynchronous optimization algorithm for distributed learning that efficiently reduces the communications between a master and working machines by randomly sparsifying the local updates, and identifies near-optimal sparsity patterns, so that all communications eventually become sparse.

Distributed Learning with Sparse Communications by Identification

- Computer ScienceSIAM J. Math. Data Sci.
- 2021

It is shown that this algorithm converges linearly in the strongly convex case and also identifies optimal strongly sparse solutions and proposes an automatic dimension reduction, aptly sparsifying all exchanges between coordinator and workers.

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks

- Computer ScienceArXiv
- 2019

A framework which allows to choose the stepsize and the momentum parameters of these algorithms in a way to optimize performance by systematically trading off the bias, variance, robustness to gradient noise and dependence to network effects is developed.

DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate

- Computer Science, MathematicsAISTATS
- 2020

A distributed asynchronous quasi-Newton algorithm that can achieve superlinear convergence guarantees is developed, believed to be the first distributed asynchronous algorithm with super linear convergence guarantees to be developed.

Advances in Asynchronous Parallel and Distributed Optimization

- Computer ScienceProceedings of the IEEE
- 2020

This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods,where each processor maintains a local copy ofThe analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods.

Sparse Asynchronous Distributed Learning

- Computer ScienceICONIP
- 2020

An asynchronous distributed learning algorithm where parameter updates are performed by worker machines simultaneously on a local sub-part of the training data with a better convergence rate and much less parameter exchanges between the master and the worker machines than without using the sparsification technique.

L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method

- Computer Science2021 60th IEEE Conference on Decision and Control (CDC)
- 2021

This work proposes a distributed algorithm for solving empirical risk minimization problems, called L-DQN, under the master/worker communication model, which is the first distributed quasi-Newton method with provable global linear convergence guarantees in the asynchronous setting where delays between nodes are present.

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