ArticlePeerJ. Computer science2024
A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks.Clocks & sleep · 2026Review
- A lightweight blockchain-inspired hybrid intrusion detection system with ensemble learning for tamper-proof auditing.PloS one · 2026Article
- A Comprehensive Survey on Intrusion Detection Systems for Healthcare 5.0: Concepts, Challenges, and Practical Applications.Sensors (Basel, Switzerland) · 2025Review
- Blockchain-based zero trust networks with federated transfer learning for IoT security in industry 5.0.PloS one · 2025Article
- Implementing federated learning for privacy-preserving emotion detection in educational environments.Frontiers in artificial intelligence · 2025Article
- EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare.Frontiers in big data · 2025Article
- Federated learning for digital twin applications: a privacy-preserving and low-latency approach.PeerJ. Computer science · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.