Federated Learning for IoUT: Concepts, Applications, Challenges and Future Directions

Victor, Nancy; Chengoden, Rajeswari; Alazab, Mamoun; Bhattacharya, Sweta; Magnússon, Sindri; Maddikunta, Praveen Kumar Reddy; Ramana, Kadiyala; Gadekallu, Thippa Reddy

doi:10.1109/iotm.001.2200067

Cited by 21 publications

(8 citation statements)

References 11 publications

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“…In addition, as discussed in [51] and [52] for the past two decades, the Underwater Internet of Things (UIoT) is widely used for developing Industrial applications such as deep-sea monitoring, diver network monitoring, early warning system, etc., among that early prediction of tsunami is much necessary to protect human life from danger. In this case, the proposed AM-BP model can be used to predict the accuracy of a tsunami by deploying and frequent collection of data from the deep-sea environment.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Thankappan,

Mary,

Yoon

et al. 2023

CMC

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Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in IoT networks. The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency. Real-world flood data is used for validation, demonstrating the superior performance of the AM-BP algorithm compared to traditional methods. In addition, multilayer high-end computing architecture (MLCA) is used to handle weather data such as rainfall, river water level, soil moisture, etc. The AM-BP's real-time abilities enable proactive flood management, facilitating timely responses and effective disaster mitigation. Furthermore, the AM-BP algorithm can analyze large and complex datasets, integrating environmental and climatic factors for more accurate flood prediction. The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%, 96.4% F1-Measure, 97% Precision, and 95.9% Recall. The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks, contributing to more resilient and efficient flood control strategies, and ensuring the safety and well-being of communities at risk of flooding.

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Section: Discussionmentioning

confidence: 99%

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Thankappan,

Mary,

Yoon

et al. 2023

CMC

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“…The healthcare industry is now undergoing a significant transformation towards a more linked, patient-centric, and collaborative approach to healthcare delivery. This transition is facilitated by the development of an expanded virtual universe that effectively incorporates virtual worlds, augmented reality, and immersive technology [12]. The integration of the metaverse facilitates the development of a vibrant healthcare ecosystem in which patients, healthcare professionals, researchers, and educators come together in a virtual reality that surpasses geographical constraints and physical obstacles.…”

Section: The Groundbreaking Paradigm Shiftmentioning

confidence: 99%

“…In addition, the incorporation of the metaverse has brought about a significant transformation in the field of medical education. This transformation surpasses the confines of conventional classroom-based instruction and instead establishes a worldwide platform that facilitates interactive and immersive learning [12]. Medical students and professionals have the opportunity to actively participate in global academic discourse, collaborate on research endeavors, and partake in immersive educational activities through virtual conferences.…”

Section: F Kilicmentioning

confidence: 99%

Revolutionizing Healthcare—The Integration of Virtual Worlds, AR, and Metaverse Technology

Kilic

2024

OJAppS

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This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical education, and research, while also addressing the challenges and opportunities they present. The paper highlights the potential benefits of these technologies and emphasizes the need for comprehensive regulatory frameworks and ethical guidelines to ensure responsible integration. Finally it outlines their transformative impact and discusses the challenges and opportunities they present for the future of healthcare provision.

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“…Aligned with healthcare problems, Victor et al 24 proposed a brain stroke prediction approach using particle swarm optimization. Next, Boughorbel et al 25 developed an FL solution for EHR‐based Preterm Birth Prediction.…”

Section: Related Workmentioning

confidence: 99%

Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

Bhatt,

Jadav,

Kumari

et al. 2023

Concurrency and Computation

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SummaryIn recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.

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Federated Learning for IoUT: Concepts, Applications, Challenges and Future Directions

Cited by 21 publications

References 11 publications

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Revolutionizing Healthcare—The Integration of Virtual Worlds, AR, and Metaverse Technology

Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

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