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A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique
Abdur Rehman, Sagheer Abbas, M. A. Khan, Taher M. Ghazal, Khan Muhammad Adnan, Amir Mosavi
TL;DR
IoMT healthcare needs improved disease prediction and remote monitoring, but centralized medical-data aggregation raises privacy and security concerns. The paper combines blockchain-based federated learning with RTS-DELM and intrusion detection, reporting 97% accuracy for its proposed healthcare 5.0 system. It concludes that the framework supports secure, privacy-preserving monitoring, prediction, and attack detection.
Problem
IoMT growth increases the need to analyze diverse medical data while addressing patient privacy breaches and adversary threats during data transmission.
Method
The paper proposes a blockchain-based federated learning healthcare 5.0 framework using RTS-DELM, encryption, medical sensors, and an intrusion detection system.
Results
97% accuracy was reported for the proposed healthcare 5.0 system, exceeding the compared published approaches in Table 7.
Takeaways & Limitations
The proposed framework is presented as supporting privacy-preserving collaborative learning, intelligent disease prediction, remote monitoring, and intrusion detection in healthcare 5.0.
Abstract
from arXiv · showhide
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases.
1. Introduction
The introduction frames IoMT and Healthcare 5.0 as enabling remote, intelligent care while emphasizing privacy and security challenges. It proposes combining blockchain, federated learning, encryption, and intrusion detection for secure disease prediction and monitoring.
- Motivation: IoMT connects medical objects to collect health data and support remote, real-time monitoring by healthcare providers.The system can track heart rate, ECG, blood pressure, temperature, and falls through wireless connections.
- Motivation: In-home smart healthcare can connect remote patients with urban clinicians and support disease discovery, treatment, and patient care.It addresses travel, queuing, and infection risks associated with routine hospital visits.
- Security and privacy: Blockchain provides cryptographic linkage, decentralization, transparency, and inflexibility, while federated learning trains shared models without exchanging actual data.These technologies are presented as responses to healthcare security, privacy, and data-sharing concerns.
- Healthcare 5.0: Healthcare 5.0 uses fifth-generation communication, IoT, automation, and AI for diagnosis, virtual monitoring, remote surgery, and intelligent treatment.Its stated focus includes patient and worldwide quality of life.
- Security and privacy: The identified obstacles include attackers inferring secret healthcare data, falsified clinical data, and insufficient motivation for medical devices to participate in federated learning.The paper addresses these issues through federated learning, sophisticated encryption, and a blockchain-based framework.
- Proposed approach: The proposed system combines blockchain-based federated learning, RTS-DELM, and an intrusion detection system to support secure disease prediction and detect attack patterns.It also evaluates secrecy, validity, accessibility, and resource overhead.
2. Literature Review
The literature review surveys blockchain applications in smart healthcare and federated learning approaches for biomedical data. It positions the proposed model within prior work on healthcare integration, resource sharing, disease prediction, and privacy-preserving collaborative learning.
- Blockchain applications: Prior blockchain research examined healthcare transactions, home healthcare, investment distribution, and peer-to-peer resource-sharing applications.The reviewed work also considered blockchain implementation and stakeholder-oriented frameworks for smart healthcare.
- Blockchain applications: Related smart healthcare studies included deep-learning diabetic disease prediction and conceptual frameworks for blockchain-enabled healthcare systems.These studies addressed implementation, impacts, and application development in smart healthcare.
- Federated learning: Federated learning reviews examined statistical, systems, and privacy challenges in biomedical applications while discussing healthcare implications and potential.Other surveyed work mapped machine learning and bioinformatics research using bibliometric visualization and Web of Science data.
- Comparative review: The review includes a comparison of existing literature with the proposed model.The supplied table caption identifies this comparison but does not provide its row-level findings.
3. Proposed Methodology
The proposed methodology combines blockchain, federated learning, RTS-DELM, data fusion, IoMT sensing, and intrusion detection for secure healthcare monitoring and disease prediction. It trains and coordinates models while retaining local medical data and processing information for Parkinson’s prediction and intrusion detection.
- Blockchain foundation: The blockchain architecture uses linked blocks, cryptography, decentralization, transparency, and proof of work to support healthcare data protection.Blocks include transaction information, previous-block hashes, nonces, and timestamps.
- RTS-DELM module: RTS-DELM analyzes real-time healthcare data, excludes data errors, and supports adaptive disease prediction and diagnosis.The proposed client model uses an input layer, six hidden layers, an output layer, sigmoid hidden-neuron activations, and backpropagation-based weight updates.
- Federated learning: Federated learning trains a shared global model through a central server while hospitals and organizations retain their local data.The approach avoids combining participant data into a centralized training collection and preserves data localization.
- Data fusion module: The system combines sensor data because multiple sources can provide more precise and trustworthy observations than a single sensor.IoMT sensors support healthcare monitoring, including cardiac-rate monitoring through heart-monitor sensors.
- Intrusion detection: An intrusion detection system uses RTS-DELM to analyze data flows and identify infiltration and assault patterns in the healthcare network.The method is designed to address centralized-security issues and potential future threats in smart blockchain-based healthcare applications.
- Federated workflow: The server-side and client-side pseudocode coordinates federated RTS-DELM training, model dissemination, healthcare monitoring, and validation.Hospitals assign local training tasks, upload local models to a centralized server, and receive a disseminated global model; validation forwards input parameters to evaluation.
4. Simulation Results
The study evaluates the proposed secure healthcare 5.0 system using intrusion detection at client sides and federated-learning disease prediction at the server side. Training and validation use separate records to assess intrusion-detection and Parkinson’s disease prediction performance.
- The evaluation uses Parkinson’s disease data for prediction and NSL-KDD data for intrusion detection, with 70% training and 30% validation/testing.
- The intrusion-detection experiment trains on 400 normal and attack records at each client side, H1–H4.
- During training, client accuracy ranges from 93.75% at H1 to 97.75% at H3, alongside reported sensitivity, specificity, predictive-value, and error-rate measures.
- The validation experiment evaluates 200 records at each client side and reports client-specific intrusion-detection accuracy and related statistical measures.
- Federated learning combines locally trained client models into a global model, which is imported from a blockchain-centered server for server-side Parkinson’s disease validation after IDS access verification.
5. Discussion
The proposed healthcare 5.0 system is compared with previously published Parkinson’s disease prediction approaches. Its reported accuracy exceeds the listed alternatives.
- The proposed healthcare 5.0 system achieves 97% accuracy for Parkinson’s disease prediction.
- The listed previous approaches report accuracies from 84.5% for Chang et al.’s CNN to 95.16% for Kuresan et al.’s HMM and SVM.
- The comparison table reports that the proposed approach outclasses the other listed approaches in accuracy.
6. Conclusion
The paper addresses privacy, security, and data-mining challenges in IoMT-based intelligent healthcare with a blockchain-entangled federated-learning system. It reports strong predictive performance for the proposed RTS-DELM method.
- IoMT-based intelligent healthcare must manage expanding medical data while addressing privacy breaches, cyberattacks, information-security concerns, and service quality.
- The paper explores blockchain technology entangled with federated learning to improve predictive performance in secure healthcare 5.0.
- The proposed RTS-DELM method is reported as extremely effective, with a stated disease-prediction accuracy of 93.22 percent.