# Asynchronous Decentralized SGD with Quantized and Local Updates

@inproceedings{Nadiradze2019AsynchronousDS, title={Asynchronous Decentralized SGD with Quantized and Local Updates}, author={Giorgi Nadiradze and Amirmojtaba Sabour and Peter Davies and Shigang Li and Dan Alistarh}, booktitle={Neural Information Processing Systems}, year={2019} }

Decentralized optimization is emerging as a viable alternative for scal-able distributed machine learning, but also introduces new challenges in terms of synchronization costs. To this end, several communication-reduction techniques, such as non-blocking communication, quantization, and local steps, have been explored in the decentralized setting. Due to the complexity of analyzing optimization in such a relaxed setting, this line of work often assumes global communication rounds, which require…

## 9 Citations

### Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD

- Computer ScienceArXiv
- 2022

This paper proposes an algorithm incorporating decentralized distributed memory computing architecture with each node running multiprocessing parallel shared-memory SGD itself, and proves that the method guarantees ergodic convergence rates for non-convex objectives.

### Consensus Control for Decentralized Deep Learning

- Computer ScienceICML
- 2021

It is shown in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart, and empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop.

### Hydra: An Optimized Data Systemfor Large Multi-Model Deep Learning [Information System Architectures]

- Computer Science
- 2022

A suite of techniques to optimize system efficiency holistically are proposed, including a highly general parameter-spilling design that enables large models to be trained even with a single GPU, a novel multi-query optimization scheme that blends model execution schedules efficiently and maximizes GPU utilization, and a double buffering idea to hide latency.

### Asynchronous Decentralized Learning over Unreliable Wireless Networks

- Computer ScienceICC 2022 - IEEE International Conference on Communications
- 2022

This work proposes an asynchronous decentralized stochastic gradient descent algorithm, robust to the inherent computation and communication failures occurring at the wireless network edge, and theoretically analyze its performance and establishes a non-asymptotic convergence guarantee.

### A Field Guide to Federated Optimization

- Computer ScienceArXiv
- 2021

This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance.

### QuAFL: Federated Averaging Can Be Both Asynchronous and Communication-Efficient

- Computer ScienceArXiv
- 2022

This work jointly addresses two of the main practical challenges when scaling federated optimization to large node counts: the need for tight synchronization between the central authority and individual computing nodes, and the large communication cost of transmissions between thecentral server and clients.

### SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

- Computer ScienceArXiv
- 2022

Theoretically, it is proved that S WIFT matches the gold-standard iteration convergence rate O (1 / √ T ) of parallel stochastic gradient descent for convex and non-convex smooth optimization (total iterations T ), and theoretical results for IID andnon-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms.

### Hybrid Decentralized Optimization: First- and Zeroth-Order Optimizers Can Be Jointly Leveraged For Faster Convergence

- Computer ScienceArXiv
- 2022

This work essentially shows that, under reasonable parameter settings, a hybrid decentralized optimization system can not only withstand noisier zeroth-order agents, but can even benefit from integrating such agents into the optimization process, rather than ignoring their information.

### Topology-aware Generalization of Decentralized SGD

- Computer ScienceICML
- 2022

It is proved that the consensus model learned by D-SGD is O ( m/N +1 /m + λ 2 ) -stable in expectation in the non-convex non-smooth setting, which is non-vacuous even when λ is closed to 1, in con-trast to vacuous as suggested by existing literature.

### Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

- Computer ScienceArXiv
- 2022

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

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