Federated Learning for Electronic Health Records

Dang, Trung Kien; Lan, Xiang; Weng, Jianshu; Feng, Mengling

doi:10.1145/3514500

Cited by 46 publications

(16 citation statements)

References 63 publications

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“…[ [73][74][75][76] Lab data Mortality A model to predict mortality in intensive care units using FL was built.…”

Section: Data Typesmentioning

confidence: 99%

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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“…[ [73][74][75][76] Lab data Mortality A model to predict mortality in intensive care units using FL was built.…”

Section: Data Typesmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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“…Te experimental results revealed that Fed-DNN-Debugger signifcantly improved the model performance and efectively fxed the federated model (Table 2). for each user n from 0 to N in parallel do (5) w t+1 n ⟵ ClientDebugger(n, w t ) (6) end for (7)…”

Section: Debugging Federated Modelsmentioning

confidence: 99%

“…To obtain the two parameters, Table 3 shows the experimental results of the model performance with diferent proportions of the selected data in the retraining dataset. From left to right, the table shows the dataset, proportion of selected high-quality samples in the retraining dataset, and (in columns [3][4][5][6][7][8][9][10][11] the number of retraining samples. We can observe that the model achieved the best performance when the ratio was 0.3 and the number of retraining samples was 4000.…”

Section: Debugging Federated Modelsmentioning

confidence: 99%

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Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning

Duan

Liu

Han

et al. 2023

Security and Communication Networks

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Federated learning is a distributed machine learning framework that has been widely applied in scenarios that require data privacy. To obtain a neural network model that performs well, when the model falls into a bug, existing solutions retrain it on a larger training dataset or the carefully selected samples from model diagnosis. To overcome this challenge, this paper presents Fed-DNN-Debugger, which can automatically and efficiently fix DNN models in federated learning. Fed-DNN-Debugger fixes the federated model by fixing each client model. Fed-DNN-Debugger consists of two modules for debugging a client model: nonintrusive metadata capture (NIMC) and automated neural network model debugging (ANNMD). NIMC collects the metadata with deep learning software syntax automatically. It does not insert any code for metadata collection into modeling scripts. ANNMD scores samples according to metadata and searches for high-quality samples. Models are retrained on the selected samples to repair their weights. Our experiments with popular federated models show that Fed-DNN-Debugger can improve the test accuracy by 8% by automatically fixing models.

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“…EHR is considered one of the prominent sources of healthcare data for FL applications. A detailed survey of existing works on EHR data for FL applications is outlined in [226]. Training an ML/AI algorithm on EHR data from a single hospital might introduce some amount of bias and may not be generalizable.…”

Section: B Statistical Challengesmentioning

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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Federated Learning for Electronic Health Records

Cited by 46 publications

References 63 publications

Survey of Medical Applications of Federated Learning

Survey of Medical Applications of Federated Learning

Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in Federated Learning

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

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