# Asynchronous Stochastic Optimization Robust to Arbitrary Delays

@inproceedings{Cohen2021AsynchronousSO, title={Asynchronous Stochastic Optimization Robust to Arbitrary Delays}, author={Alon Cohen and Amit Daniely and Yoel Drori and Tomer Koren and Mariano Schain}, booktitle={NeurIPS}, year={2021} }

We consider stochastic optimization with delayed gradients where, at each time step π‘ , the algorithm makes an update using a stale stochastic gradient from step π‘ β π π‘ for some arbitrary delay π π‘ . This setting abstracts asynchronous distributed optimization where a central server receives gradient updates computed by worker machines. These machines can experience computation and communication loads that might vary significantly over time. In the general non-convex smooth optimizationβ¦Β

## 4 Citations

### Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning

- Computer ScienceArXiv
- 2022

The asynchronous stochastic gradient descent algorithm for distributed training over n workers which have varying computation and communication frequency over time is studied and it is shown for the first time that asynchronous SGD is always faster than mini-batch SGD.

### Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

- Computer ScienceArXiv
- 2022

This work introduces a novel recursion based on βvirtual iteratesβ and delay-adaptive stepsizes, which allow it to derive state-of-theart guarantees for both convex and non-convex objectives.

### Distributed Distributionally Robust Optimization with Non-Convex Objectives

- Computer Science
- 2022

An asynchronous distributed algorithm, named ASPIRE, is proposed with the EASE method to tackle the distributed distributionally robust optimization (DDRO) problem and can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

### Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback

- Computer ScienceArXiv
- 2022

This paper presents the first algorithms that achieve near-optimal β K +D regret, where K is the number of episodes and D = βK k=1 d k is the total delay, significantly improving upon the best known regret bound of (K +D).

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