Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare

Gupta, Deepti; Kayode, Olumide; Bhatt, Smriti; Gupta, Maanak; Tosun, Ali Şaman

doi:10.48550/arxiv.2111.12241

Cited by 5 publications

(10 citation statements)

References 25 publications

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“…While several researchers include one network segment or a whole hospital in a local model, Gupta et al propose a digital twin for each patient and train their local model on it. The advantage is that all collected data belong to a single patient, and the researchers can correlate more parameters [59]. Specification-based detection approaches are the second largest group, though by a large margin (9).…”

Section: A Rq 1 -Utilized Detection Approach and Employed Technologymentioning

confidence: 99%

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

et al. 2023

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The threat situation due to cyber attacks in hospitals is emerging and patient life is at risk. One significant source of potential vulnerabilities is medical cyber-physical systems (MCPS). Detecting intrusions in this environment faces challenges different from other domains, mainly due to the heterogeneity of devices, the diversity of connectivity types, and the variety of terminology. To summarize existing results, we conducted a structured literature review (SLR) following the guidelines of Kitchenham et al. for SLRs in software engineering. We developed six research questions regarding detection approach, detection location, included features, adversarial focus, utilized datasets, and intrusion prevention. We identified that most researchers focused on an anomaly-based detection approach at the network layer. The primary focus was on the detection of malicious insiders. While several researchers used publicly available datasets for training and testing their algorithms, the lack of suitable datasets resulted in the development of testbeds consisting of various medical devices. Based on the results, we formulated five future research topics. First, the special conditions of hospital networks, the MCPS deployed within them, and the contrasts to other IT and OT environments should be examined. Thereupon, MCPS-specific datasets should be created that allow researchers to address the health domain's unique requirements and possibilities. At the same time, endeavors aimed at standardization in this area should be supported and expanded. Moreover, the use of medical context for attack detection should be further explored. Last but not least, efforts for MCPS-tailored intrusion prevention should be intensified. This way, the emerging threat landscape can be addressed, IT security in hospitals can be improved, and patient health can be protected.

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Section: A Rq 1 -Utilized Detection Approach and Employed Technologymentioning

confidence: 99%

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

et al. 2023

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“…It is now possible to find the application of digital twins (DT) in many fields, and one such application is discussed in [71] in the area of IoMT. DT is said to be the digital replica of physical process and there is continuous data and information sharing between both entities.…”

Section: Fl Enabled Digital Twin For Iomtmentioning

confidence: 99%

“…Furthermore, the incorporation of DT in IoMT enables secure remote patient monitoring (RPM). The long short term memory (LSTM) based anomaly detection was built-in [71] and FL based DT was used for preventing adversaries from getting private information.…”

Section: Fl Enabled Digital Twin For Iomtmentioning

confidence: 99%

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

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Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to endusers as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacyrelated issues in IoMT. Afterward, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities of FL in IoMT. In the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems.

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“…The advantages that AI can bring to medicine have been predicted for decades. The role of AI in biomedical engineering has even been reviewed [18]. AI and its applications in healthcare have recently made considerable strides [19].…”

Section: Introductionmentioning

confidence: 99%

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

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Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

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Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare

Cited by 5 publications

References 25 publications

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Attack Detection for Medical Cyber-Physical Systems–A Systematic Literature Review

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

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