Blockchain Meets Federated Learning in Healthcare: A Systematic Review With Challenges and Opportunities

Myrzashova, Raushan; Alsamhi, Saeed Hamood; Shvetsov, Alexey V.; Hawbani, Ammar; Wei, Xi

doi:10.1109/jiot.2023.3263598

Cited by 64 publications

(9 citation statements)

References 106 publications

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“…The integration of FL with blockchain technology is a promising development, which forms the central theme of this tutorial. This combination not only bolsters data security but also adds a layer of transparency and traceability to the FL process, ensuring immutable recordkeeping and verifiable model updates in FL networks [412].…”

Section: E1 Current Trends In Fl For Healthcarementioning

confidence: 99%

Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

Shahsavari,

Dambri,

Baseri

et al. 2024

Preprint

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Section: E1 Current Trends In Fl For Healthcarementioning

confidence: 99%

Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

Shahsavari,

Dambri,

Baseri

et al. 2024

Preprint

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“…Data were gathered from several users or devices in a federated learning healthcare environment. These users or devices can represent diverse demographics, behaviors, and preferences [145,146]. As a result, there can be variations in the patterns, characteristics, and distributions of the data gathered from healthcare.…”

Section: Challenges and Directionsmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti,

Yang,

Kim

et al. 2024

CMES

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Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources, such as patient records, medical imaging, wearable devices, and clinical research surveys. This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare. It evaluates the effectiveness of federated learning strategies in the field of healthcare. It offers a systematic analysis of federated learning in the healthcare domain, encompassing the evaluation metrics employed. In addition, this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

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“…Blockchain enabled FL Kim and Hong 2019;Bao et al 2019) aims to create a secure and transparent way of training machine learning models across multiple participants while ensuring privacy, data ownership, and trust. In a blockchain enabled FL system (Chai et al 2020;Ma et al 2021;Myrzashova et al 2023), the training data remains with the participants, and only the model updates are shared with the blockchain network. The blockchain acts as a decentralized ledger that records model updates, ensuring transparency and immutability.…”

Section: Fl Framework Empowered By Blockchainmentioning

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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Blockchain Meets Federated Learning in Healthcare: A Systematic Review With Challenges and Opportunities

Cited by 64 publications

References 106 publications

Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

Integration of Federated Learning and Blockchain in Healthcare: A Tutorial

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

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

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