Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model

Bhattacharya, Amartya Kumar; Gawali, Manish; Seth, Jitesh; Kulkarni, Viraj

doi:10.48550/arxiv.2204.10505

Cited by 2 publications

(1 citation statement)

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“…The authors in [160] introduced a collaborative FL framework that allows multiple medical institutions to use DL to screen COVID-19 from chest X-ray images without sharing/exchanging the patient data. Similarly, there have been several studies that propose the use of FL for COVID-19 X-ray data training and deploy experiments to verify the FL effectiveness [161,162,163,164,165,166,167,168,169,58,170,171,172,173].…”

Section: Covid-19 Related Studies On Fl In Healthcarementioning

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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Section: Covid-19 Related Studies On Fl In Healthcarementioning

confidence: 99%

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

Rauniyar¹,

Hagos²,

Jha³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Machine learning: A multifaceted exploration of trends, regulations, and global impact

Rishwinder,

Tusharika,

Santosh

et al. 2024

JFET

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The field of Machine Learning (ML) demands a comprehensive exploration encompassing research advancements, industry applications, and emerging regulatory considerations. This article delves into these multifaceted aspects, identifying key trends and challenges that are shaping the landscape of ML. The literature reveals that machine learning is rapidly transforming various industries. For instance, in healthcare, ML algorithms achieve accuracy rates exceeding 90% in medical image analysis, leading to earlier diagnoses and improved patient outcomes. Similarly, in nanotechnology, ML is employed to design and optimize novel materials, enhancing properties by approximately 50% compared to traditional methods. However, the ethical and legal implications of Artificial Intelligence (AI) and machine learning necessitate careful consideration. The article explores ongoing discussions surrounding regulations and responsible development in this domain. By offering a comprehensive perspective that integrates advancements, applications, and regulatory considerations, this analysis aims to serve as a valuable resource for academics and policymakers navigating the complexities and opportunities associated with machine learning.

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Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model

Cited by 2 publications

References 0 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

Machine learning: A multifaceted exploration of trends, regulations, and global impact

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