Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices

Houssein, Essam H.; Sayed, Awny

doi:10.1016/j.compbiomed.2023.107195

Cited by 11 publications

(6 citation statements)

References 42 publications

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“…For the purposes of feature selection and dimensionality reduction, the imputed data (i.e., normalized medical data record) is collected at this stage 44 , 45 . Since the GFS technique offers the best and most optimal way to choose the most significant characteristics from the available data, it is employed in this study to accomplish this purpose.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared.

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Section: Proposed Methodologymentioning

confidence: 99%

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

Nandagopal,

Seerangan,

Govindaraju

et al. 2024

Sci Rep

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“…In addition to these wireless communication protocols, other protocols, such as MQTT and Constrained Application Protocol (CoAP), are used for efficient data transmission in IoT systems. These protocols are designed to minimise network overhead and power consumption while ensuring the reliable delivery of data [39,51,63]. IoT applications frequently employ MQTT, a lightweight publish-subscribe communications protocol.…”

Section: Iot and Wsnsmentioning

confidence: 99%

“…FL techniques can be applied to analyse medical data collected from multiple sources while preserving data privacy. Yet, before these technologies are widely used in healthcare, issues including data security, interoperability, and regulatory compliance must be resolved [40,63,67].…”

Section: Applications Of Integrated Iot Wsns and Flmentioning

confidence: 99%

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Mengistu,

Kim,

Lin

2024

Sensors

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Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

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“…Our plan is to use the ability of IoT devices to work together to make heart disease prediction models more accurate while protecting the privacy of patients' EHR data. We also [4] want to look into how different IoT device properties, like the amount of data they send and receive and their processing power, affect the shared learning process. This study adds to the fields of healthcare and machine learning in a number of ways.…”

Section: Introductionmentioning

confidence: 99%

Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach

Pranjal Pandit, Yogesh J. Gaikwad, Amruta Mhatre

2024

jes

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Predicting heart diseases is important for finding them early and treating them effectively. We present a shared learning method for predicting heart diseases using IoT-based electronic health records (EHRs) in this work. Federated learning lets many autonomous IoT devices work together to train a model, while protecting the safety and security of the data. Proposed method uses the fact that IoT devices are spread out to train a global model for predicting heart disease without putting private EHR data in one place. With the EHR data, each IoT device learns a model locally and only sends model changes to a central computer. The computer takes all of these changes and improves the world model. This model is then sent back to the IoT devices to be improved even more. This looping process makes sure that the world model keeps getting better while keeping data private. The proposed method tested by using a real-world collection of EHRs from IoT devices in trials. We looked at how well our shared learning method worked compared to more standard centralized learning methods. Our results show that the pooled learning method makes predictions that are as good as or better than the other methods while protecting data privacy. It also looked at how different IoT device properties, like the amount of data they send and receive and their processing power, affect the shared learning process. It is discovered that devices with more processing power and more data add more to the improvement of the global model. This shows how important it is to choose the right devices in shared learning systems. The paper study shows that pooled learning can be used to predict heart diseases in IoT-based EHRs and that it works well. Our method uses the ability of IoT devices to work together to make accurate predictions while protecting data privacy. This makes it suitable for use in real-life healthcare situations.

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Boosted federated learning based on improved Particle Swarm Optimization for healthcare IoT devices

Cited by 11 publications

References 42 publications

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach

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