Integration of Federated Learning and Blockchain in Healthcare: A Tutorial DOI Creative Commons
Yahya Shahsavari, Oussama Abderrahmane Dambri, Yaser Baseri

et al.

Published: April 18, 2024

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in Machine Learning (ML) for healthcare.This tutorial explores Federated (FL) Blockchain (BC) integration, offering a secure privacy-preserving approach to healthcare analytics.FL enables decentralized model training on local at institutions, keeping patient localized.This facilitates collaborative development without compromising privacy.However, FL introduces vulnerabilities.BC, with its tamper-proof ledger smart contracts, provides robust framework learning FL.After presenting taxonomy the various types of used ML applications, concise review techniques use cases, this three integration architectures balancing decentralization, scalability, reliability data.Furthermore, it investigates how Blockchain-based (BCFL) enhances security collaboration disease prediction, image analysis, drug discovery.By providing FL, blockchain, their along BCFL paper serves as valuable resource researchers practitioners seeking leverage these technologies ML.It aims accelerate advancements analytics, ultimately improving outcomes.

Language: Английский

Integration of Federated Learning and Blockchain in Healthcare: A Tutorial DOI Creative Commons
Yahya Shahsavari, Oussama Abderrahmane Dambri, Yaser Baseri

et al.

Published: April 18, 2024

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in Machine Learning (ML) for healthcare.This tutorial explores Federated (FL) Blockchain (BC) integration, offering a secure privacy-preserving approach to healthcare analytics.FL enables decentralized model training on local at institutions, keeping patient localized.This facilitates collaborative development without compromising privacy.However, FL introduces vulnerabilities.BC, with its tamper-proof ledger smart contracts, provides robust framework learning FL.After presenting taxonomy the various types of used ML applications, concise review techniques use cases, this three integration architectures balancing decentralization, scalability, reliability data.Furthermore, it investigates how Blockchain-based (BCFL) enhances security collaboration disease prediction, image analysis, drug discovery.By providing FL, blockchain, their along BCFL paper serves as valuable resource researchers practitioners seeking leverage these technologies ML.It aims accelerate advancements analytics, ultimately improving outcomes.

Language: Английский

Citations

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