4 research outputs found
A Survey From Distributed Machine Learning to Distributed Deep Learning
Artificial intelligence has achieved significant success in handling complex
tasks in recent years. This success is due to advances in machine learning
algorithms and hardware acceleration. In order to obtain more accurate results
and solve more complex problems, algorithms must be trained with more data.
This huge amount of data could be time-consuming to process and require a great
deal of computation. This solution could be achieved by distributing the data
and algorithm across several machines, which is known as distributed machine
learning. There has been considerable effort put into distributed machine
learning algorithms, and different methods have been proposed so far. In this
article, we present a comprehensive summary of the current state-of-the-art in
the field through the review of these algorithms. We divide this 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 studies worked on this algorithms. As a
result, most of the articles we discussed here belong to this category. Based
on our investigation of algorithms, we highlight limitations that should be
addressed in future research