Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application

Yoo, Joo Hun; Son, Ha Min; Jeong, Hyejun; Jang, Eun-Hye; Kim, Ah-Young; Yu, Hong; Jeon, Hong Jin; Chung, Tai-Myoung

doi:10.1109/ictc52510.2021.9620852

Cited by 27 publications

(12 citation statements)

References 12 publications

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“…The prevalent use of CNNs is likely due to the fact that image data were the most common type of data in these studies. Additionally, CNNs have been applied to the analysis of signal data [63,64,66]. The optimization method most frequently used was Adam, which was adopted in 35 studies.…”

Section: Discussionmentioning

confidence: 99%

“…Research on FL using signal data has largely concentrated on disease research involving heart-activity data or the development of health-monitoring systems. The identified objectives for FL research using signal data included predicting the severity of major depressive disorder based on heart rate variability [63], detecting arrhythmias through electrocardiography [64], automatically detecting stress using heart-activity signals [65], implementing wearable healthcare solutions [66], monitoring health at home [67], and developing health-monitoring systems that employ wearable sensing devices [68].…”

Section: Data Typesmentioning

confidence: 99%

“…A significant number of these studies, 35 to be exact, utilized convolutional neural networks (CNNs) as their primary model [81]. CNNs were predominantly used in research involving image and signal data [63,64,66]. In contrast, studies that focused on free text primarily implemented recurrent neural networks, including long short-term memory networks [82], and some incorporated the more recent BERT [83].…”

Section: Utilization Of Neural Network Models In Flmentioning

confidence: 99%

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Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain.Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security.Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns.Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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Section: Discussionmentioning

confidence: 99%

Section: Data Typesmentioning

confidence: 99%

Section: Utilization Of Neural Network Models In Flmentioning

confidence: 99%

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Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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“…In addition, they compared the classification accuracy by increasing the number of clients used in federated learning and stated that FedREMCS could be adopted in various health-care fields based on these results. Moreover, Yoo generated a major depressive disorder severity classifier with heart rate variability data collected at Seoul Samsung Medical Center (Yoo et al , 2021). They used the clustering-based federated learning method, Personalized Federated Cluster Model, to mitigate the nonidentically distributed (IID) problem and demonstrated higher accuracy compared to Federated Averaging.…”

Section: Federated Learning In Medical Applicationsmentioning

confidence: 99%

“…Based on (ibid. ), Yoo et al (2021) used heart rate variability data of patients to diagnose depression severity. They applied a clustering-based federated learning algorithm called personalized federated learning with clustering for new incoming participants to improve prediction accuracy and solve nonIID issues.…”

Section: Research Issuesmentioning

confidence: 99%

Open problems in medical federated learning

Yoo

Jeong

Lee

et al. 2022

IJWIS

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Purpose This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment. Design/methodology/approach Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions. Findings This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution. Originality/value Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

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