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Healthcare Informatics Security Issues and Solutions Using Federated Learning
Abstract
This chapter investigates the complex security challenges that arise in healthcare informatics, with a particular emphasis on data protection and confidentiality. This demonstrates the need for encryption and restricting access to delicate medical information. Ransomware attacks and data breaches in the healthcare industry are among the cyber security dangers discussed in this study. Through the implementation of stringent cyber security standards, audits, and people training, the organization strives to increase security. The organization also considers interoperability and the exchange of confidential health information across different healthcare systems. We advise using standardized protocols and secure communication techniques to enhance data transfer and ensure its safety. It is possible to secure medical equipment from hackers by implementing stringent security standards, performing regular updates, and separating networks. Additionally, the research investigates how human factors and insider threats create vulnerabilities in healthcare informatics systems. As preventive measures, it emphasizes staff education, marketing that raises user awareness, and behavioral analytics. This study may assist healthcare professionals, policymakers, and academics in protecting patient data and ensuring the integrity of the system. As a result of the rising use of technology and the digitalization of healthcare data, the study concludes that it is necessary to comprehend and address the security concerns that are associated with healthcare informatics. Through the implementation of these measures, stakeholders are able to enrich the healthcare ecosystem.
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“Data Privacy and Security: FL enables institutions to train models without sharing sensitive patient information, aligning with regulations like HIPAA and GDPR [46].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [46]
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Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228
Federated Machine Learning to Enable Intrusion Detection Systems in IoT Networks
Devine, M., Ardakani, S. P., Al-Khafajiy, M., et al. (2025) · Electronics · doi:10.3390/electronics14061176 · Cited by 27
AI Workload Automation and Orchestration in Cloud Environments
Goswami, S. A., Patel, K. C. K., Darji, D. A., Patel, S., & Patel, S. J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9694-0.ch002 · Cited by 5
Self-citationChaos-Based Dynamic Authentication for Secure Telehealth in Smart Cities
Nofal, M., & Elmanfaloty, R. A. (2025) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling6020025 · Cited by 3
Federated Learning Approaches for Privacy-Preserving XSS Detection
Bharath, M. B., Chandrakala., L., N, D. K., et al. (2025) · doi:10.1109/ic3it66137.2025.11341683 · Cited by 2
Metadata
- Authors
- Goswami, S. A., Dave, S., & Patel, K. C.
- Venue
- Federated Learning for Smart Communication using IoT Application
- Publisher
- Chapman and Hall/CRC
- Published
- 2024-09-20
- Pages
- 124-154
- OpenAlex
- W4402673657
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1
- FWCI
- 5.10
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Health Sciences › Health Professions › Health Information Management › Thoreau and American Literature
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- federated learning, data confidentiality, encryption, access control, ransomware, data breaches, interoperability, health information exchange, medical device security, insider threats, behavioral analytics
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- CoChat. (2026). Citation check: Healthcare Informatics Security Issues and Solutions Using Federated Learning. https://app.cochat.ai/papers/10.1201/9781003489368-7
- The paper
- Goswami, S. A., Dave, S., & Patel, K. C. (2024). Healthcare Informatics Security Issues and Solutions Using Federated Learning. Federated Learning for Smart Communication using IoT Application. https://doi.org/10.1201/9781003489368-7