Deep Federated Learning for IoT-based Decentralized Healthcare Systems

Elayan, Haya; Aloqaily, Moayad; Guizani, Mohsen

doi:10.1109/iwcmc51323.2021.9498820

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…The model's performance under the IoMT devices' computing heterogeneity was not addressed. [44] ResNet-50 Atlas Dermatology dataset The FTL framework outperforms the non-TFL approach in terms of the AUC (Area Under The Curve) metric and maintaining the same accuracy.…”

Section: Fl For Covid-19mentioning

confidence: 98%

“…The experiment results demonstrate that FedHealth can improve the classification performance compared to the non-federated model and the traditional machine learning model. In a similar work, Elayan et al [44] proposed a TFL framework using IoMT devices in order to detect skin diseases. The results demonstrate that the FTL outperforms the non-TFL approach in terms of the Area Under The Curve (AUC) metric and maintains the same accuracy.…”

Section: A Federated Transfer Learningmentioning

confidence: 99%

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Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

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Section: Fl For Covid-19mentioning

confidence: 98%

Section: A Federated Transfer Learningmentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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“…An efficient and privacy-preserving FL with irrelevant updates framework was suggested, where a non-interactive key generation algorithm was used to reduce the negative impact of irrelevant updates, speed up model convergence, and improve prediction accuracy [65]. To ensure user privacy in dispersed healthcare systems, a paper [66] suggested a DFL framework. To solve the issue of the restricted availability of healthcare data for developing DL models, the paper describes an experiment for applying DFL to identify skin disorders [66].…”

Section: B Privacy and Preserving Challengesmentioning

confidence: 99%

“…To ensure user privacy in dispersed healthcare systems, a paper [66] suggested a DFL framework. To solve the issue of the restricted availability of healthcare data for developing DL models, the paper describes an experiment for applying DFL to identify skin disorders [66]. Another paper proposed a unique FDFF-based algorithm called double deep Q-network (DDQN) that is made possible by an integrated system called SMEC, which offers a reliable method for determining realtime treatment policy from a large number of dispersed observational EMRs and ensures the confidentiality of EMRs via additively homomorphic encryption [67].…”

Section: B Privacy and Preserving Challengesmentioning

confidence: 99%

A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions

et al. 2023

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Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and February 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies.

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“…The framework utilizes differential privacy and homomorphic encryption for guaranteeing preserved privacy, and it was mainly built for regression for genomics data. In [88] the authors proposed a framework to train on skin lesion images using IoT devices (smartphones). They further utilized Transfer Learning in this Federated Learning framework to circumvent the need of large, labelled data.…”

Section: Federated Learning Frameworkmentioning

confidence: 99%

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

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Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.

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Deep Federated Learning for IoT-based Decentralized Healthcare Systems

Cited by 31 publications

References 16 publications

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

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