Federated Learning: A Comprehensive Review of Models, Algorithms, and Business Applications

Year : 2024 | Volume :14 | Issue : 03 | Page : –
By

Nagendra Pratap Singh,

Mamata Singh,

  1. Associate Dean-Research, Department of Techno Centre Engineering, MS Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India
  2. Visiting Senior Scientist, Department of Medicine, Division of Infectious Disease, Mayo Clinic, Jacksonville, Florida, USA

Abstract

‘]

In an age where data privacy is a significant concern, Federated Learning (FL) has become a game-changing method in machine learning. This decentralized model enables various parties to work together on training models without exchanging their raw data, effectively tackling the issues posed by data silos and privacy regulations. This paper reviews the current state of FL, including its underlying models and algorithms, practical applications, benefits, challenges, and future directions and NVIDIA’s Clara Train SDK plays a crucial role in FL. By synthesizing recent research findings, we aim to provide a comprehensive understanding of FL’s impact on various industries and its potential to drive innovation. The article highlights the Federated Learning process. The paper reviews the current landscape of FL in various business domains, addressing its benefits, challenges, and prospects, ultimately emphasizing its role in creating a more efficient and privacy-preserving healthcare ecosystem. Federated Learning represents a significant leap forward in collaborative innovation, allowing businesses to leverage collective intelligence while maintaining data privacy. This decentralized approach opens new opportunities across various sectors, driving significant value and enabling organizations to balance privacy with powerful insights. As research and development in this area progress, Federated Learning is set to become a fundamental pillar in the future evolution of machine learning.

Keywords: Federated Learning (FL), machine learning, artificial intelligence (AI), global model, Secure Aggregation

[This article belongs to Current Trends in Information Technology (ctit)]

How to cite this article:
Nagendra Pratap Singh, Mamata Singh. Federated Learning: A Comprehensive Review of Models, Algorithms, and Business Applications. Current Trends in Information Technology. 2024; 14(03):-.
How to cite this URL:
Nagendra Pratap Singh, Mamata Singh. Federated Learning: A Comprehensive Review of Models, Algorithms, and Business Applications. Current Trends in Information Technology. 2024; 14(03):-. Available from: https://journals.stmjournals.com/ctit/article=2024/view=171692



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Regular Issue Subscription Original Research
Volume 14
Issue 03
Received August 5, 2024
Accepted August 28, 2024
Published September 11, 2024
Views: 330

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