pFedKT: Personalized federated learning with dual knowledge transfer

Knowledge-Based Systems, Год журнала: 2024, Номер 292, С. 111633 - 111633

Опубликована: Март 13, 2024

Язык: Английский

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Cybersecurity for Sustainable Smart Healthcare: State of the Art, Taxonomy, Mechanisms, and Essential Roles

Deleted Journal, Год журнала: 2024, Номер 4(2), С. 20 - 62

Опубликована: Май 23, 2024

Cutting-edge technologies have been widely employed in healthcare delivery, resulting transformative advances and promising enhanced patient care, operational efficiency, resource usage. However, the proliferation of networked devices data-driven systems has created new cybersecurity threats that jeopardize integrity, confidentiality, availability critical data. This review paper offers a comprehensive evaluation current state context smart healthcare, presenting structured taxonomy its existing cyber threats, mechanisms essential roles. study explored (SHSs). It identified discussed most pressing attacks SHSs face, including fake base stations, medjacking, Sybil attacks. examined security measures deployed to combat SHSs. These include cryptographic-based techniques, digital watermarking, steganography, many others. Patient data protection, prevention breaches, maintenance SHS integrity are some roles ensuring sustainable healthcare. The long-term viability depends on constant assessment risks harm providers, patients, professionals. aims inform policymakers, practitioners, technology stakeholders about imperatives best practices for fostering secure resilient ecosystem by synthesizing insights from multidisciplinary perspectives, such as cybersecurity, management, sustainability research. Understanding recent is controlling escalating networks encouraging intelligent delivery.

Язык: Английский

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Enhancing medical image classification via federated learning and pre-trained model

Egyptian Informatics Journal, Год журнала: 2024, Номер 27, С. 100530 - 100530

Опубликована: Авг. 28, 2024

Язык: Английский

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Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Healthcare, Год журнала: 2024, Номер 12(24), С. 2587 - 2587

Опубликована: Дек. 22, 2024

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, remote monitoring, which empower real-time, decentralized data processing for predictive analytics personalized care. It addresses key challenges, including security risks like adversarial attacks, poisoning, model inversion. Additionally, it covers issues related to heterogeneity, scalability, system interoperability. Alongside these, the highlights emerging privacy-preserving solutions, such as differential secure multiparty computation, critical overcoming limitations. Successfully addressing these hurdles essential enhancing efficiency, accuracy, broader adoption in healthcare. Ultimately, FL offers transformative potential secure, data-driven promising improved outcomes, operational sovereignty ecosystem.

Язык: Английский

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Artificial Intelligence-Enabled Internet of Medical Things (AIoMT) in Modern Healthcare Practices

Advances in medical technologies and clinical practice book series, Год журнала: 2024, Номер unknown, С. 42 - 69

Опубликована: Июнь 7, 2024

The integration of artificial intelligence (AI), the internet things (IoT), with medical devices avails recent development in sector, specifically digital health, referred to as (IoMT). AIoMT combines technologies like body movement detection, sleep monitoring, and rehab assessment, simplifying healthcare offering personalized experiences. By leveraging AI, big data, mobile internet, cloud computing, microelectronics, patient data is efficiently processed, enhancing healthcare's efficiency personalization. During pandemic, AI applications saved lives by streamlining analysis. This chapter explores wearable electronics sensor architecture addresses challenges security, aiming elevate standards. It also outlines future research opportunities AIoMT.

Язык: Английский

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Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things

Sensors, Год журнала: 2024, Номер 24(18), С. 5937 - 5937

Опубликована: Сен. 13, 2024

This study investigates the efficacy of machine learning models for intrusion detection in Internet Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating performance ensemble algorithms, specifically Stacking, Bagging, Boosting, using Random Forest Support Vector Machines as base WUSTL-EHMS-2020 dataset. Through a comprehensive examination metrics such accuracy, precision, recall, F1-score, Stacking demonstrates exceptional accuracy reliability detecting classifying cyber attack incidents with an rate 98.88%. Bagging is ranked second, 97.83%, while Boosting yielded lowest 88.68%.

Язык: Английский

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Exploring Key Considerations for Artificial Intelligence Robots in Home Healthcare Using the Unified Theory of Acceptance and Use of Technology and the Fuzzy Analytical Hierarchy Process Method

Systems, Год журнала: 2025, Номер 13(1), С. 25 - 25

Опубликована: Янв. 2, 2025

Most countries face declining birth rates and an aging population, which makes the persistent healthcare labor shortage a pressing challenge. Introducing artificial intelligence (AI) robots into home could help address these issues. Exploring primary considerations for integrating AI in has become urgent topic. However, previous studies have not systematically examined factors influencing elderly individuals’ adoption of robots, hindering understanding their acceptance adoption. Furthermore, traditional methods overlook relative importance each consideration cannot manage ambiguity inherent subjective human cognition, potentially leading to biased decision-making. To limitations, this study employs unified theory use technology (UTAUT) as theoretical framework, modified Delphi method (MDM) fuzzy analytical hierarchy process (FAHP) identify key considerations. The research determined order four evaluation criteria fourteen sub-criteria, revealing that customization, accompany, norms are influence robots.

Язык: Английский

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Federated Learning in Agents Based Cyber-Physical Systems

Опубликована: Янв. 1, 2025

Язык: Английский

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IoMT: A Medical Resource Management System Using Edge Empowered Blockchain Federated Learning

IEEE Transactions on Network and Service Management, Год журнала: 2023, Номер 21(1), С. 517 - 534

Опубликована: Авг. 24, 2023

As data sharing on the Internet of Medical Things (IoMT) become more complicated, problems divergent interests, unregulated policies, privacy and security, resource constraints owners have drawn attention researchers. To address problems, this paper provides management in IoMT using a proposed edge-empowered blockchain federated learning system. Also, an improved linear regressor model is as global for Gradient parameters are encrypted Paillier encryption server side before they shared by clients. Blockchain deployed to provide new security features edge computing. Moreover, all transactions devices stored secure cataloguing auditing. Edge computing employed handle complex tasks behalf devices. Extensive simulations conducted validate efficacy system model. The results show that costs minimized while still achieving benefits Furthermore, analysis shows protected from attacks.

Язык: Английский

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An Intelligent and Explainable SaaS-Based Intrusion Detection System for Resource-Constrained IoMT

IEEE Internet of Things Journal, Год журнала: 2023, Номер 11(15), С. 25454 - 25463

Опубликована: Окт. 24, 2023

The Internet of Medical Things (IoMT) has revolutionized healthcare, but its vulnerabilities demand robust security solutions, especially for resource-constrained devices. In this research, we introduce an innovative Software as a Service (SaaS)-based Intrusion Detection System (IDS) designed specifically the unique challenges IoMT, deploying at edge enhanced efficiency. Our proposed IDS incorporates multi-faceted approach: Firstly, it leverages Particle Swarm Optimization (PSO) algorithm feature engineering, optimizing data representation to reduce computational overhead on Secondly, diverse ensemble machine learning and deep models is employed detect wide array intrusion attempts within IoMT networks. Thirdly, interpretation achieved using SHapley Additive exPlanations (SHAP), providing transparency understanding decision-making process. By combining intelligence, efficiency, explainability, SaaS solution network edge, our not only bolsters devices also empowers healthcare professionals with actionable insights, ensuring patient privacy integrity in dynamic critical domain. Finally, results publicly available dataset namely WUSTL-EHMS-2020 proves effectiveness over some recent state-of-the-art works.

Язык: Английский

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