The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning

2023/12/05 by Omer Subasi, Subasi, Omer, Oceane Bel +5
Computer Science · #Artificial Intelligence (cs.AI) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2312.03120

openalex publication_date 2023/12/05 · openalex created_date 2023/12/08 · openalex updated_date 2026/08/01

Abstract

With the advance of the powerful heterogeneous, parallel and distributed computing systems and ever increasing immense amount of data, machine learning has become an indispensable part of cutting-edge technology, scientific research and consumer products. In this study, we present a review of modern machine and deep learning. We provide a high-level overview for the latest advanced machine learning algorithms, applications, and frameworks. Our discussion encompasses parallel distributed learning, deep learning as well as federated learning. As a result, our work serves as an introductory text to the vast field of modern machine learning.

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