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Parul Agarwal

Work place: Department of Computer Science and Engineering, School of Engineering Sciences and Technology (SEST), Jamia Hamdard University, Hamdard Nagar, New Delhi, Delhi 110062, India

E-mail: pagarwal@jamiahamdard.ac.in

Website: https://orcid.org/0000-0002-7297-335X

Research Interests:

Biography

Dr Parul Agarwal She is associated with Jamia Hamdard since 2002. She is currently a Professor, Dean, school of engineering sciences and technology, and also heading the Department of Computer Science and Engineering at Jamia Hamdard. Her area of specialization include Fuzzy Data Mining, Cloud Computing, Sustainable computing, and Soft Computing. She has published several papers in reputed and SCI, Scopus and Elsevier indexed journals and many book chapters published by CRC press, Springer, IGI-Global are to her credit. She has edited one book with CRC , one book edited with IGI GLOBAL, and three others with Springer (all SCOPUS indexed). She is the guest editor of several Special issues of reputed Scopus and SCI indexed journals. She has chaired several sessions of International/ National Conferences of repute. Currently, several PhD. Scholars are working under her supervision in the field of Brain Signalling, Energy minimization in Cloud, Time series forecasting, and Smart Building management for smart cities. She has organized a workshop as Principal Investigator in 2017, on “Use of ICT in sustainable Computing” approved by Department of Science and Technology, Govt. of India, worth 5.75 Lakhs. She has guided more than 80 Masters thesis (MCA and M.Tech.). Member of several committees at university level and membership of several professional bodies like ISTE, and senior member IEEE are to her credit.

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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