Experiments of Federated Learning for COVID-19 Chest X-ray Images

Liu, Boyi; Yan, Bingjie; Zhou, Yonggang; Yang, Yifan; Zhang, Yixian

doi:10.48550/arxiv.2007.05592

Cited by 35 publications

(38 citation statements)

References 12 publications

(11 reference statements)

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“…Liu [34] and Zhang [65] conducted experiment on diagnosing COVID-19 in both federated and centralized learning, demonstrating that both prediction accuracy are comparable. Choudhury conducted experiments to predict Adverse Drug Reactions with Electronic Health Records dataset [12] and compared the results of that used centralized learning, localized learning, and federated learning.…”

Section: Physical Disorder Predictionsmentioning

confidence: 99%

Federated Learning: Issues in Medical Application

Yoo¹,

Jeong²,

Lee³

et al. 2021

Preprint

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Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data remain in local sites. However, federated learning techniques still have various open issues due to its own characteristics such as non identical distribution, client participation management, and vulnerable environments. In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The framework will be open to public after development completes.

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Section: Physical Disorder Predictionsmentioning

confidence: 99%

Federated Learning: Issues in Medical Application

Yoo¹,

Jeong²,

Lee³

et al. 2021

Preprint

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“…By emphasising on the fact that patients data across different medical centres should be handled privately, FL setting is the natural option for such applications. Therefore, Liu et al [141] applied FL to datasets of various clinical centres; FL clients exploited the available local X-ray images of Covid-19 cases at each hospital to train a model that helps practitioners to determine if a patient has been infected, without leaking any personal information.…”

Section: ) Iot Networkmentioning

confidence: 99%

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan¹,

Mohjazi²,

Bariah³

et al. 2021

Preprint

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The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

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“…Simulations from 34,006 CT scan slices (images) of 89 subjects verify a high COVID-19 image classification and low data loss in FL algorithm running. FL is also used in [129] to provide privacy-preserved AI solutions for COVID-19 chest Xray image analytics. Several practical experiments have been implemented where multiple COVID-19 CXR image owners run local learning networks such as ResNet18 for image classification and then share the computed parameters with a data center for mobile averaging while the data ownership of each user is ensured.…”

Section: Smart Industrymentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen,

Ding,

Pathirana

et al. 2021

Preprint

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The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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Experiments of Federated Learning for COVID-19 Chest X-ray Images

Cited by 35 publications

References 12 publications

Federated Learning: Issues in Medical Application

Federated Learning: Issues in Medical Application

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Federated Learning for Internet of Things: A Comprehensive Survey

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