Cyber resilience through adaptive federated learning
This chapter explores the soon-to-be-reached intersection of cyber resilience and adaptive federated learning (FL), presenting a thorough examination of how FL, and especially through its adaptability mechanisms, can significantly enhance an organizations ability to predict, withstand, recover from, and learn from cyber-attacks. It explores the fundamental concepts of cyber resilience and FL, outlines several adaptive methodologies utilized in FL (e.g., adaptive client selection, aggregation, and optimization), and examines their direct contribution to the formation of resilient and privacy-aware cybersecurity systems. Real-world applications in critical infrastructures, accompanied by an honest review of current limitations and research avenues towards the future, present the revolutionary potential embedded within this synergy-based approach in an increasingly sophisticated and interlinked digital era. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors.
Neetha, S.S.; Singh, Shivangi; Deepa, S.
Federated Learning in Finance: Unlocking Privacy-Preserving and Cyber Resilience using AI;pp.187-214
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https://doi.org/10.1201/9781003660330-9 https://www.scopus.com/pages/publications/105035273312?origin=resultslist
Neetha S.S., Department of Computer Science, Christ University, Bangalore, India; Singh S., Department of Computer Science, Christ University, Bangalore, India; Deepa S., Department of Computer Science, Christ University, Bangalore, India