Rise of Federated Learning to Real-World Applications

Shaligram Prajapat, Shubhi Gehlot, Dishita Naik, Nitin Naik

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Federated Learning (FL) emerges as a beacon of promise in addressing privacy concerns tethered to centralized machine learning. This innovative approach allows model training on diverse devices, fostering collaborative learning while zealously guarding the confidentiality of raw data. Our paper embarks on a journey through seven practical domains— Healthcare, Finance, Education, Browsing Behavior, Retail and E-Commerce, Natural Language Processing, and Recommendation Systems—revealing the transformative potential of FL without compromising data security. As we traverse these real-world landscapes, the simplicity of FL unfolds, harmonizing collaborative learning with the imperative of data privacy. This exploration beckons a future where machine learning and data-driven collaborations seamlessly navigate the realms of privacy, fostering a landscape rich with innovation and collaborative potential.
Original languageEnglish
Title of host publicationContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK: The C3AI 2024
EditorsNitin Naik, Paul Jenkins, Shaligram Prajapat, Paul Grace
Pages699-719
Number of pages21
ISBN (Electronic)9783031744433
DOIs
Publication statusPublished - 20 Dec 2024

Publication series

NameLecture Notes in Networks and Systems
Volume884 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Keywords

  • Data Privacy
  • Distributed Computing
  • Federated Learning (FL)
  • Machine Learning (ML)

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