A Survey From Distributed Machine Learning to Distributed Deep Learning

TL;DR

This survey reviews the evolution from distributed machine learning to distributed deep learning, highlighting key algorithms, classifications, and future research challenges in the field.

Contribution

It provides a comprehensive overview of distributed deep learning, categorizing existing algorithms and identifying current limitations for future exploration.

Findings

Distributed deep learning has gained significant attention recently.

Algorithms are categorized into classification, clustering, deep learning, and deep reinforcement learning.

The survey highlights limitations and future research directions in distributed deep learning.

Abstract

Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be time-consuming and require a great deal of computation. To address these issues, distributed machine learning has been proposed, which involves distributing the data and algorithm across several machines. There has been considerable effort put into developing distributed machine learning algorithms, and different methods have been proposed so far. We divide these algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of the studies have focused on this approach. Therefore, we mostly concentrate on this category. Based on the investigation of the mentioned algorithms, we highlighted the limitations that should be addressed in future research.