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

Ali, Mansoor; Naeem, Faisal; Tariq, Muhammad; Kaddoum, Geroges

doi:10.48550/arxiv.2203.09702

Cited by 3 publications

(9 citation statements)

References 71 publications

(83 reference statements)

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“…Ref [26], [40] • Describes the role of FL in Internet of Medical Things (IoMT) networks for privacy preservation.…”

Section: Publications Main Research Focus and Scopementioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar,

Hagos,

Jha

et al. 2024

IEEE Internet Things J.

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.

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“…Ref [26], [40] • Describes the role of FL in Internet of Medical Things (IoMT) networks for privacy preservation.…”

Section: Publications Main Research Focus and Scopementioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar,

Hagos,

Jha

et al. 2024

IEEE Internet Things J.

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“…FL has a promising future in the medical sector as a revolutionary technique for ensuring data privacy (Xu et al 2021;Lee and Shin 2020;Silva et al 2019;Chen et al 2020;Lim et al 2020;Ali et al 2022). Smart healthcare based on FL has empowered clinical diagnostics.…”

Section: Medical Applicationsmentioning

confidence: 99%

“…It can can solve the problem of inadequate healthcare, particularly regarding rare diseases (Nguyen et al 2022;Dinh et al 2023;Zhou et al 2023). It safeguards patient privacy and achieves greater medical resources at a reasonable cost (Catarinucci et al 2015;Nguyen et al 2022;Ali et al 2022). The FL framework is used to acquire and analyze biomedical data without revealing sensitive patient information Silva et al 2019;Sharghi et al 2015).…”

Section: Medical Applicationsmentioning

confidence: 99%

A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

Saha,

Hota,

Chattopadhyay

et al. 2024

Artif Intell Rev

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Federated learning (FL) refers to a system of training and stabilizing local machine learning models at the global level by aggregating the learning gradients of the models. It reduces the concern of sharing the private data of participating entities for statistical analysis to be carried out at the server. It allows participating entities called clients or users to infer useful information from their raw data. As a consequence, the need to share their confidential information with any other entity or the central entity called server is eliminated. FL can be clearly interpreted as a privacy-preserving version of traditional machine learning and deep learning algorithms. However, despite this being an efficient distributed training scheme, the client’s sensitive information can still be exposed to various security threats from the shared parameters. Since data has always been a major priority for any user or organization, this article is primarily concerned with discussing the significant problems and issues relevant to the preservation of data privacy and the viability and feasibility of several proposed solutions in the FL context. In this work, we conduct a detailed study on FL, the categorization of FL, the challenges of FL, and various attacks that can be executed to disclose the users’ sensitive data used during learning. In this survey, we review and compare different privacy solutions for FL to prevent data leakage and discuss secret sharing (SS)-based security solutions for FL proposed by various researchers in concise form. We also briefly discuss quantum federated learning (QFL) and privacy-preservation techniques in QFL. In addition to these, a comparison and contrast of several survey works on FL is included in this work. We highlight the major applications based on FL. We discuss certain future directions pertaining to the open issues in the field of FL and finally conclude our work.

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“…Most of the existing survey studies such as [24,25,26,27,28,29,31,32] are confined to explaining either general architecture, models, security, and privacy algorithms for FL or explaining how FL can be used in conjugation with other emerging technologies. Since FL has been successfully applied in conjugation with other emerging technologies, there is hardly a few survey work or studies such as [33,34,35,36] that have looked into FL and providing an overview on works that have tackled especially in the medical domain. Therefore, the focus of this survey is solely on the integration of FL with other emerging technologies for medical applications.…”

Section: A Scope and Contributionsmentioning

confidence: 99%

“…Ref [34,35] • Describes the role of FL in Internet of Medical Things (IoMT) networks for privacy preservation.…”

Section: Publications Main Research Focus and Scopementioning

confidence: 99%

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

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With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/Deep Learning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications have attracted a great deal of attention from academia to industry. This has undoubtedly improved the quality of healthcare delivery. However, AI-based medical applications still have poor adoption due to their difficulties in satisfying strict security, privacy, and quality of service standards such as low latency. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, this survey paper highlights the current and future of FL technology in medical applications where data sharing is a significant burden. It also review and discuss the current research trends and their outcomes for designing reliable and scalable FL models. We outline the general FL's statistical problems, device challenges, security, privacy concerns, and its potential in the medical domain. Moreover, our study is also focused on medical applications where we highlight the burden of global cancer and the efficient use of FL for the development of computer-aided diagnosis tools for addressing them. Additionally, the recent literature revealed that FL models ensure a high level of robustness and generalization compared to the traditional data-driven medical applications. We hope that this review serves as a checkpoint that sets forth the existing state-of-the-art works in a thorough manner and offers open problems and future research directions for this field.

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

Cited by 3 publications

References 71 publications

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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