Communication bottleneck has been identified as a significant issue in distributed
optimization of large-scale learning models. Recently, several approaches to mitigate this …

Communication bottleneck has been identified as a significant issue in distributed
optimization of large-scale learning models. Recently, several approaches to mitigate this …

Gradient sparsification is a promising technique to significantly reduce the communication
overhead in decentralized synchronous stochastic gradient descent (S-SGD) algorithms …

Gradient compression (eg, gradient quantization and gradient sparsification) is a core
technique in reducing communication costs in distributed learning systems. The recent trend …

The large communication overhead has imposed a bottleneck on the performance of
distributed Stochastic Gradient Descent (SGD) for training deep neural networks. Previous …

Huge scale machine learning problems are nowadays tackled by distributed optimization
algorithms, ie algorithms that leverage the compute power of many devices for training. The …

The large communication cost for exchanging gradients between different nodes
significantly limits the scalability of distributed training for large-scale learning models …

A standard approach in large scale machine learning is distributed stochastic gradient
training, which requires the computation of aggregated stochastic gradients over multiple …

Modern large-scale machine learning applications require stochastic optimization
algorithms to be implemented on distributed computational architectures. A key bottleneck is …

In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm
for decentralized training of large-scale machine learning models over a network. In …