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

December 05, 2023 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning"

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Authors Omer Subasi, Oceane Bel, Joseph Manzano, Kevin Barker arXiv ID 2312.03120 Category cs.LG: Machine Learning Cross-listed cs.AI, cs.DC Citations 2 Venue arXiv.org Last Checked 4 days ago
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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