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Kamlesh Kumar Raghuvanshi

Work place: Department of Computer Science and Engineering, Jamia Hamdard University, Hamdard Nagar, New Delhi, Delhi 110062, India

E-mail: mailto.kamlesh@ramanujan.du.ac.in

Website: https://orcid.org/0000-0001-9887-3392

Research Interests:

Biography

Dr. Kamlesh Kumar Raghuvanshi He is an Assistant Professor in the Department of Computer Science at Ramanujan College, University of Delhi. He possesses more than 23 years of professional experience, including 14 years in teaching and research and 9 years in the information technology industry. He earned his Ph.D. in Computer Science and has established himself as an academician, researcher, and technology professional with expertise in software engineering, enterprise applications, ERP solutions, educational technology, and digital transformation. Throughout his academic career, he has been actively involved in teaching undergraduate and postgraduate courses, mentoring students, and contributing to curriculum development and institutional growth. Dr. Raghuvanshi has published more than 15 research papers in reputed national and international journals and conference proceedings. He is the author of four books and has successfully guided two research scholars. His research interests include software engineering, enterprise resource planning systems, e-governance, artificial intelligence applications, and technology-enabled education. Prior to joining academia, he worked with leading multinational organizations, including IBM, Tata Consultancy Services (TCS), Wipro Technologies, and Tech Mahindra, gaining extensive experience in software development, project management, and enterprise solutions. His rich industry background enables him to effectively bridge the gap between academic learning and industry requirements.

Author Articles
FL-EZTF: A Privacy-Preserving Federated Deep Learning Framework with Enhanced Zero Trust for Healthcare IoT Security

By Urvashi Parul Agarwal Kamlesh Kumar Raghuvanshi Jawed Ahmed

DOI: https://doi.org/10.5815/ijem.2026.04.22, Pub. Date: 8 Aug. 2026

Smart healthcare IoT systems are vulnerable to cyber threats as they deal with sensitive patient information. Problems such as privacy, scalability, and delayed response to threats in distributed healthcare environments challenge centralized security approaches. To mitigate the security challenges of cloud-edge healthcare IoT systems, this paper presents FL-EZTF, a privacy-preserving, Federated Deep Learning and Enhanced Zero Trust Framework. The framework combines federated learning, Enhanced Zero Trust Architecture (E-ZTA), and Secure Access Service Edge (SASE). In this framework, lightweight deep learning models are developed locally at hospitals and various edge nodes without the need to transfer sensitive medical data. In place of raw data, model updates are sent conveniently through a trustaware federated learning process. Simultaneously, E-ZTA performs continuous authentication, micro-segmentation, and access control to rapidly contain threats. The framework is assessed using CIC-IoT-2023, IoT-23, and WESAD datasets. The experimental results show improved accuracy in detection, lower rates of false positives, a significant reduction in the latency of decisions, and enhanced containment as compared to centralized and traditional federated learning.

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