Federated Learning for Smart Healthcare: A Survey

Nguyen, Dinh C.; Pham, Quoc-Viet; Pathirana, Pubudu N.; Ding, Ming; Seneviratne, Aruna; Lin, Zihuai; Dobre, Octavia A.; Hwang, Won-Joo

doi:10.48550/arxiv.2111.08834

Cited by 4 publications

(7 citation statements)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following that, the latest FL ideas for intelligent healthcare are reviewed, which includes FL for resource management, FL aware of security concerns, incentive FL, and tailored FL. Following that, we present a cutting edge overview of FL's developing applications in major healthcare areas such as data management, distant health monitoring, biomedical image analytics, and so on [9,158,159].…”

Section: Taxonomies Of Fl With Ai In Healthcarementioning

confidence: 99%

“…Federated Learning, a new distributed interactive AI concept, is especially promising for smart healthcare since it allows numerous clients (such as Hospitals) to participate on AI training whilst maintaining data privacy. As a result, the authors investigated the application of FL in smart healthcare extensively [9]. To begin, we will discuss current breakthroughs in FL, as well as the reasons and prerequisites for adopting FL in smart healthcare.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been undertaken to study FL based AI related issues, including healthcare, as a result of recent advancements in the field. The publications in [9], for example, provide the basic FL idea and its supporting protocols, as well as the technical hurdles of FL design and implementation. [20] covers the security and privacy challenges in FL systems, as well as potential methods for assessing harmful threats in FL networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

View full text Add to dashboard Cite

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.

show abstract

Section: Taxonomies Of Fl With Ai In Healthcarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The end nodes then train the model based on their local data, and the updated model is then shared back with a central model for aggregation. For instance, we can use a federated average model for aggregation, where the weights are assigned to local model parameters based on the data size availability [45]. In the end, the new global model is computed and is shared back with the end nodes.…”

Section: Fl and Its Perspective In Iomtmentioning

confidence: 99%

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

Ali¹,

Naeem²,

Tariq³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to endusers as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacyrelated issues in IoMT. Afterward, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities of FL in IoMT. In the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems.

show abstract

“…Nguyen et al [32] discuss advanced FL designs that would be useful for federated smart healthcare, as well as the important applications of FL in smart healthcare, such as federated EHRs management, federated remote health monitoring, federated medical imaging, and federated COVID-19 detection.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics

Kandati

Gadekallu

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Healthcare is predominantly regarded as a crucial consideration in promoting the general physical and mental health and well-being of people around the world. The amount of data generated by healthcare systems is enormous, making it challenging to manage. Many machine learning (ML) approaches were implemented to develop dependable and robust solutions to handle the data. ML cannot fully utilize data due to privacy concerns. This primarily happens in the case of medical data. Due to a lack of precise clinical data, the application of ML for the same is challenging and may not yield desired results. Federated learning (FL), which is a recent development in ML where the computation is offloaded to the source of data, appears to be a promising solution to this problem. In this study, we present a detailed survey of applications of FL for healthcare informatics. We initiate a discussion on the need for FL in the healthcare domain, followed by a review of recent review papers. We focus on the fundamentals of FL and the major motivations behind FL for healthcare applications. We then present the applications of FL along with recent state of the art in several verticals of healthcare. Then, lessons learned, open issues, and challenges that are yet to be solved are also highlighted. This is followed by future directions that give directions to the prospective researchers willing to do their research in this domain.

show abstract

Federated Learning for Smart Healthcare: A Survey

Cited by 4 publications

References 118 publications

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

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

A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics

Contact Info

Product

Resources

About