Federated Learning: Issues in Medical Application

Yoo, Joo Hun; Jeong, Hyejun; Lee, Jaehyeok; Chung, Tai-Myoung

doi:10.1007/978-3-030-91387-8_1

Cited by 17 publications

(22 citation statements)

References 35 publications

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“…Review papers on FL in the medical domain have been published [15][16][17][18][19]; however, these studies have only introduced a limited number of examples of medical FL research. Our study differs from existing FL reviews by concentrating on specific instances of medical FL research.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

“…To extract insights from the papers we reviewed, we organized the studies according to several criteria: (1) According to previous literature reviews on FL [15][16][17]20], heterogeneity and security concerns were frequently discussed. Therefore, we explored the extent to which studies addressed these issues.…”

Section: Methodsmentioning

confidence: 99%

Survey of Medical Applications of Federated Learning

Choi,

Cha,

Lee

et al. 2024

Healthc Inform Res

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“…In the context of the gradual maturity of machine learning and the realization of automatic identification and intelligent decision-making, in order to solve the problem of data privacy protection, federated learning (13)(14)(15)(16)) emerged as a potential solution.…”

Section: Federated Learningmentioning

confidence: 99%

An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning

Zhang

et al. 2022

Front. Oncol.

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Since the 20th century, cancer has been a growing threat to human health. Cancer is a malignant tumor with high clinical morbidity and mortality, and there is a high risk of recurrence after surgery. At the same time, the diagnosis of whether the cancer is in situ recurrence is crucial for further treatment of cancer patients. According to statistics, about 90% of cancer-related deaths are due to metastasis of primary tumor cells. Therefore, the study of the location of cancer recurrence and its influencing factors is of great significance for the clinical diagnosis and treatment of cancer. In this paper, we propose an assisted diagnosis model for cancer patients based on federated learning. In terms of data, the influencing factors of cancer recurrence and the special needs of data samples required by federated learning were comprehensively considered. Six first-level impact indicators were determined, and the historical case data of cancer patients were further collected. Based on the federated learning framework combined with convolutional neural network, various physical examination indicators of patients were taken as input. The recurrence time and recurrence location of patients were used as output to construct an auxiliary diagnostic model, and linear regression, support vector regression, Bayesling regression, gradient ascending tree and multilayer perceptrons neural network algorithm were used as comparison algorithms. CNN’s federated prediction model based on improved under the condition of the joint modeling and simulation on the five types of cancer data accuracy reached more than 90%, the accuracy is better than single modeling machine learning tree model and linear model and neural network, the results show that auxiliary diagnosis model based on the study of cancer patients in assisted the doctor in the diagnosis of patients, As well as effectively provide nutritional programs for patients and have application value in prolonging the life of patients, it has certain guiding significance in the field of medical cancer rehabilitation.

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“…Secondly, cross-silo FL assumes more data and higher compute power at the nodes, so local computational constraints do not impact the computations as severely. In medical systems, practitioners might want to avoid approximation errors at the cost of higher compute time [15]. In distributed memory contexts, diverse schemes have been proposed to efficiently and quickly compute the QR decomposition (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…The reason for this is the fact that the initial vector norm of the QR step is not technically an aggregate. We visualize the aggregation step in Algorithm 7 in Equation (15), as it is the motivation for our investigation. To avoid ambiguity, we denote the resulting upper triangular matrix S with elements s i,j .…”

Section: Privacy Of Federated Gram-schmidt On Upper Triangular Matricesmentioning

confidence: 99%

Privacy of federated QR decomposition using additive secure multiparty computation

Hartebrodt¹,

Röttger²

2022

Preprint

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Federated learning (FL) is a privacy-aware data mining strategy keeping the private data on the owners' machine and thereby confidential. The clients compute local models and send them to an aggregator which computes a global model. In hybrid FL, the local parameters are additionally masked using secure aggregation, such that only the global aggregated statistics become available in clear text, not the client specific updates. Federated QR decomposition has not been studied extensively in the context of cross-silo federated learning. In this article, we investigate the suitability of three QR decomposition algorithms for cross-silo FL and suggest a privacy-aware QR decomposition scheme based on the Gram-Schmidt algorithm which does not blatantly leak raw data. We apply the algorithm to compute linear regression in a federated manner.

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Federated Learning: Issues in Medical Application

Cited by 17 publications

References 35 publications

Survey of Medical Applications of Federated Learning

Survey of Medical Applications of Federated Learning

An Assisted Diagnosis Model for Cancer Patients Based on Federated Learning

Privacy of federated QR decomposition using additive secure multiparty computation

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