Leveraging Federated Learning for Enhanced Privacy in Cybersecurity Applications: A Comparative Study of Traditional vs. AI-Driven Approaches

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Atika Nishat
Hadia Azmat

Abstract

This research paper investigates the use of federated learning as an innovative approach to enhance privacy in cybersecurity applications. It compares traditional privacy-preserving techniques with AI-driven methods, focusing on the strengths, weaknesses, and potential implications for data security and user privacy. By analyzing case studies and recent advancements, this paper aims to provide a comprehensive overview of how federated learning can reshape privacy considerations in the cybersecurity landscape.

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How to Cite
Atika Nishat, & Hadia Azmat. (2024). Leveraging Federated Learning for Enhanced Privacy in Cybersecurity Applications: A Comparative Study of Traditional vs. AI-Driven Approaches. Pioneer Research Journal of Computing Science, 1(1), 96–109. Retrieved from https://prjcs.com/index.php/prjcs/article/view/13

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