Federated Deep Learning for Internet of Medical Things: A Comprehensive Survey of Architectures, Healthcare Applications, Privacy Preservation, Security, and Future Research Directions | IJCT Volume 13 – Issue 4 | IJCT-V13I4P11
International Journal of Computer Techniques
ISSN 2394-2231
Author
Dr. Vijaysinh G. Chavan, Dr. Tukaram A. Chavan
Abstract
The rapid integration of the Internet of Things (IoT) into healthcare has enabled continuous patient monitoring, remote diagnosis, personalized treatment, and real-time clinical decision support. Internet of Medical Things (IoMT) devices, including wearable sensors, smart medical equipment, and remote monitoring systems, generate large-scale heterogeneous healthcare data that can significantly improve artificial intelligence (AI)-based medical analytics. However, conventional centralized deep learning approaches require transferring sensitive patient data to cloud servers, creating substantial privacy, security, and regulatory challenges.
Federated Deep Learning (FDL) provides an emerging solution by enabling distributed healthcare entities to collaboratively train deep learning models while keeping patient data localized. Instead of sharing raw medical information, healthcare IoT devices exchange encrypted model parameters, thereby reducing privacy risks while maintaining collaborative intelligence. Recent research has demonstrated that federated learning can support privacy-preserving disease prediction, medical image analysis, and intelligent healthcare monitoring while addressing data sovereignty concerns [1], [2].
This paper proposes a privacy-preserving federated deep learning framework for IoT-enabled healthcare analytics. The proposed framework integrates IoT sensing devices, edge computing, federated optimization, differential privacy, secure aggregation, and adaptive deep learning models to provide intelligent medical data analysis without compromising patient confidentiality. The framework addresses major challenges including heterogeneous medical datasets, limited IoT resources, communication overhead, adversarial attacks, and model interpretability. The proposed approach provides a scalable architecture for next-generation healthcare systems supporting personalized medicine, early disease detection, and secure AI-driven clinical decision-making.
Keywords
Artificial Intelligence, Deep Neural Networks, Edge Computing, Federated Deep Learning, Healthcare Analytics, Internet of Medical Things, Privacy Preservation, Secure Aggregation.
Conclusion
Federated Deep Learning (FDL) has emerged as a promising approach for enabling privacy-preserving intelligence in Internet of Medical Things (IoMT) environments by allowing collaborative model training without sharing sensitive patient data. This survey reviewed the key architectures, healthcare applications, privacy-preserving techniques, and security mechanisms that support the development of secure and efficient IoMT-based healthcare systems. It also highlighted the major challenges, including data heterogeneity, communication overhead, resource constraints, and security threats that affect the practical deployment of federated learning in healthcare.
Although significant progress has been made, several challenges remain before Federated Deep Learning can be widely adopted in real-world healthcare applications. Future research should focus on improving communication efficiency, model robustness, scalability, and interoperability while integrating emerging technologies such as explainable AI, blockchain, edge computing, and advanced privacy-preserving techniques. Addressing these issues will enable the development of secure, intelligent, and trustworthy IoMT systems that enhance healthcare services and improve patient outcomes.
References
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How to Cite This Paper
Dr. Vijaysinh G. Chavan, Dr. Tukaram A. Chavan (2026). Federated Deep Learning for Internet of Medical Things: A Comprehensive Survey of Architectures, Healthcare Applications, Privacy Preservation, Security, and Future Research Directions. International Journal of Computer Techniques, 13(4). ISSN: 2394-2231.
Federated Deep Learning for Internet of Medical Things A Comprehensive Survey of Architectures, Healthcare Applications, Privacy Preservation, Security, and Future Research DirectionsDownload
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