A Survey From Distributed Machine Learning to Distributed Deep Learning

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. Keywords: Artificial intelligence, Machine learning, Distributed machine learning, Distributed deep learning, Ditributed reinforcement learning, Data-parallelism, Model-parallelism. Introduction Artificial intelligence (AI) is a rapidly developing field that uses knowledge to simulate human behaviors (1) and train computers to learn, make judgments, and make decisions similarly to humans (2, 3). In other words, AI involves developing techniques and algorithms that are capable of thinking, acting, and implementing tasks using protocols that are otherwise beyond human comprehension (4). Machine learning (ML) is a subset of AI that learns from historical data, without being explicitly programmed (5). ML algorithms can be used to analyze data and build data-driven systems, including classification, clustering, regression, association rule mining, and reinforcement learning (6, 7). Deep learning is a branch of machine learning that uses artificial neural networks to intelligently analyze large amounts of data (8, 9).