International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Revolutionizing Cybersecurity with AI and Federated Learning: A Privacy-Preserving Approach for Distributed System

Author(s) Syed Umer Hasan, FNU Sahil, Soorajj kumar, Ashish Shiwlani
Country Pakistan
Abstract Greater storage, processing, and communication reliance on distributed systems have led to significant cybersecurity issues. Often operating in highly diverse and decentralized environments, these systems conflict with many traditional approaches to security in terms of privacy regulations and user trust. Federated Learning has come as a novel paradigm where it is possible to execute decentralized machine learning by locally training models on data in every device, thus avoiding the transferring of data to a centralized server and ensuring privacy. When FL works in combination with AI, it is highly effective against threats for cybersecurity by enabling timely detection, anomaly identification, and proactive response mechanisms. This work investigates the integration of FL and AI for privacy-centric cybersecurity challenges in distributed systems, its architecture, use cases, and challenges, and the practical implications this approach has on reshaping secure computing paradigms. The findings underline the critical equilibrium between privacy and security while using FL-AI systems for future distributed environments.
Keywords Federated Learning, Artificial Intelligence, Privacy-Preserving, Cybersecurity, Distributed Systems, Decentralized Machine Learning, Threat Detection, Data Security, Anomaly Detection, Privacy Regulations.
Field Computer > Network / Security
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-06-07
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E-ISSN 2582-2160
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