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Ahmed R, Maddikunta PKR, Gadekallu TR, Alshammari NK, Hendaoui FA. Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images. Front Med (Lausanne) 2024; 11:1409314. [PMID: 38912338 PMCID: PMC11193384 DOI: 10.3389/fmed.2024.1409314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/15/2024] [Indexed: 06/25/2024] Open
Abstract
The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.
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Affiliation(s)
- Rawia Ahmed
- Computer Science Department, Applied College, University of Ha’il, Ha’il, Saudi Arabia
| | - Praveen Kumar Reddy Maddikunta
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Thippa Reddy Gadekallu
- The College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China
- Division of Research and Development, Lovely Professional University, Phagwara, India
- Center of Research Impact and Outcome, Chitkara University, Rajpura, India
| | - Naif Khalaf Alshammari
- Mechanical Engineering Department, Engineering College, University of Ha’il, Ha’il, Saudi Arabia
| | - Fatma Ali Hendaoui
- Computer Science Department, Applied College, University of Ha’il, Ha’il, Saudi Arabia
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Vo VTT, Shin TH, Yang HJ, Kang SR, Kim SH. A comparison between centralized and asynchronous federated learning approaches for survival outcome prediction using clinical and PET data from non-small cell lung cancer patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108104. [PMID: 38457959 DOI: 10.1016/j.cmpb.2024.108104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND AND OBJECTIVE Survival analysis plays an essential role in the medical field for optimal treatment decision-making. Recently, survival analysis based on the deep learning (DL) approach has been proposed and is demonstrating promising results. However, developing an ideal prediction model requires integrating large datasets across multiple institutions, which poses challenges concerning medical data privacy. METHODS In this paper, we propose FedSurv, an asynchronous federated learning (FL) framework designed to predict survival time using clinical information and positron emission tomography (PET)-based features. This study used two datasets: a public radiogenic dataset of non-small cell lung cancer (NSCLC) from the Cancer Imaging Archive (RNSCLC), and an in-house dataset from the Chonnam National University Hwasun Hospital (CNUHH) in South Korea, consisting of clinical risk factors and F-18 fluorodeoxyglucose (FDG) PET images in NSCLC patients. Initially, each dataset was divided into multiple clients according to histological attributes, and each client was trained using the proposed DL model to predict individual survival time. The FL framework collected weights and parameters from the clients, which were then incorporated into the global model. Finally, the global model aggregated all weights and parameters and redistributed the updated model weights to each client. We evaluated different frameworks including single-client-based approach, centralized learning and FL. RESULTS We evaluated our method on two independent datasets. First, on the RNSCLC dataset, the mean absolute error (MAE) was 490.80±22.95 d and the C-Index was 0.69±0.01. Second, on the CNUHH dataset, the MAE was 494.25±40.16 d and the C-Index was 0.71±0.01. The FL approach achieved centralized method performance in PET-based survival time prediction and outperformed single-client-based approaches. CONCLUSIONS Our results demonstrated the feasibility and effectiveness of employing FL for individual survival prediction in NSCLC patients, using clinical information and PET-based features.
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Affiliation(s)
- Vi Thi-Tuong Vo
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
| | - Tae-Ho Shin
- Interdisciplinary Program of Information Security, Chonnam National University, Gwangju, 61186, South Korea
| | - Hyung-Jeong Yang
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea
| | - Sae-Ryung Kang
- Department of Nuclear Medicine, Chonnam National University Hwasun Hospital and Medical School, Hwasun, 58128, South Korea.
| | - Soo-Hyung Kim
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea.
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Mirimoghaddam MM, Majidpour J, Pashaei F, Arabalibeik H, Samizadeh E, Roshan NM, Rashid TA. HER2GAN: Overcome the Scarcity of HER2 Breast Cancer Dataset Based on Transfer Learning and GAN Model. Clin Breast Cancer 2024; 24:53-64. [PMID: 37926662 DOI: 10.1016/j.clbc.2023.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/06/2023] [Accepted: 09/24/2023] [Indexed: 11/07/2023]
Abstract
INTRODUCTION Immunohistochemistry (IHC) is crucial for breast cancer diagnosis, classification, and individualized treatment. IHC is used to measure the levels of expression of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor receptor 2 (HER2), and other biomarkers, which are used to make treatment decisions and predict how well a patient will do. The evaluation of the breast cancer score on IHC slides, taking into account structural and morphological features as well as a scarcity of relevant data, is one of the most important issues in the IHC debate. Several recent studies have utilized machine learning and deep learning techniques to resolve these issues. MATERIALS AND METHODS This paper introduces a new approach for addressing the issue based on supervised deep learning. A GAN-based model is proposed for generating high-quality HER2 images and identifying and classifying HER2 levels. Using transfer learning methodologies, the original and generated images were evaluated. RESULTS AND CONCLUSION All of the models have been trained and evaluated using publicly accessible and private data sets, respectively. The InceptionV3 and InceptionResNetV2 models achieved a high accuracy of 93% with the combined generated and original images used for training and testing, demonstrating the exceptional quality of the details in the synthesized images.
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Affiliation(s)
| | - Jafar Majidpour
- Department of Computer Science, University of Raparin, Rania, Iraq.
| | - Fakhereh Pashaei
- Radiation Sciences Research Center (RSRC), Aja University of Medical Sciences, Tehran, Iran.
| | - Hossein Arabalibeik
- Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Samizadeh
- Department of Pathology, School of Medicine and Imam Reza Hospital, AJA University of Medical Sciences, Tehran, Iran
| | | | - Tarik A Rashid
- Computer Science and Engineering Department, University of Kurdistan Hewlêr, Erbil, Iraq
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Sandhu SS, Gorji HT, Tavakolian P, Tavakolian K, Akhbardeh A. Medical Imaging Applications of Federated Learning. Diagnostics (Basel) 2023; 13:3140. [PMID: 37835883 PMCID: PMC10572559 DOI: 10.3390/diagnostics13193140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.
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Affiliation(s)
- Sukhveer Singh Sandhu
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
| | - Hamed Taheri Gorji
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
- SafetySpect Inc., 4200 James Ray Dr., Grand Forks, ND 58202, USA
| | - Pantea Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
| | - Kouhyar Tavakolian
- Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA; (H.T.G.); (P.T.)
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Rjoub G, Wahab OA, Bentahar J, Cohen R, Bataineh AS. Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2022:1-18. [PMID: 35875592 PMCID: PMC9294770 DOI: 10.1007/s10796-022-10307-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/08/2022] [Indexed: 05/27/2023]
Abstract
In the context of distributed machine learning, the concept of federated learning (FL) has emerged as a solution to the privacy concerns that users have about sharing their own data with a third-party server. FL allows a group of users (often referred to as clients) to locally train a single machine learning model on their devices without sharing their raw data. One of the main challenges in FL is how to select the most appropriate clients to participate in the training of a certain task. In this paper, we address this challenge and propose a trust-based deep reinforcement learning approach to select the most adequate clients in terms of resource consumption and training time. On top of the client selection mechanism, we embed a transfer learning approach to handle the scarcity of data in some regions and compensate potential lack of learning at some servers. We apply our solution in the healthcare domain in a COVID-19 detection scenario over IoT devices. In the considered scenario, edge servers collaborate with IoT devices to train a COVID-19 detection model using FL without having to share any raw confidential data. Experiments conducted on a real-world COVID-19 dataset reveal that our solution achieves a good trade-off between detection accuracy and model execution time compared to existing approaches.
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Affiliation(s)
- Gaith Rjoub
- Concordia Institute for Information Systems Engineering, Concordia University, 1455 De Maisonneuve Blvd. W.2, Montreal, H3G 1M8 Quebec Canada
| | - Omar Abdel Wahab
- Department of Computer Science and Engineering, Université du Québec en Outaouais, 101, Saint-Jean-Bosco, C.P. 1250, succursale Hull, Gatineau, J8X 3X7 Quebec Canada
| | - Jamal Bentahar
- Concordia Institute for Information Systems Engineering, Concordia University, 1455 De Maisonneuve Blvd. W.2, Montreal, H3G 1M8 Quebec Canada
| | - Robin Cohen
- David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, N2L 3G1 ON Canada
| | - Ahmed Saleh Bataineh
- Concordia Institute for Information Systems Engineering, Concordia University, 1455 De Maisonneuve Blvd. W.2, Montreal, H3G 1M8 Quebec Canada
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Ali H, Shah Z. Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review. JMIR Med Inform 2022; 10:e37365. [PMID: 35709336 PMCID: PMC9246088 DOI: 10.2196/37365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/02/2022] Open
Abstract
Background Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. Methods A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as “generative adversarial networks” and “GANs,” and application keywords, such as “COVID-19” and “coronavirus.” The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies. Conclusions Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs’ performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Kossen T, Hirzel MA, Madai VI, Boenisch F, Hennemuth A, Hildebrand K, Pokutta S, Sharma K, Hilbert A, Sobesky J, Galinovic I, Khalil AA, Fiebach JB, Frey D. Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks. Front Artif Intell 2022; 5:813842. [PMID: 35586223 PMCID: PMC9108458 DOI: 10.3389/frai.2022.813842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/31/2022] [Indexed: 12/03/2022] Open
Abstract
Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.
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Affiliation(s)
- Tabea Kossen
- CLAIM-Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Computer Engineering and Microelectronics, Computer Vision & Remote Sensing, Technical University Berlin, Berlin, Germany
| | - Manuel A. Hirzel
- CLAIM-Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Vince I. Madai
- CLAIM-Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Responsible Research, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany
- Faculty of Computing, Engineering and the Built Environment, School of Computing and Digital Technology, Birmingham City University, Birmingham, United Kingdom
| | | | - Anja Hennemuth
- Department of Computer Engineering and Microelectronics, Computer Vision & Remote Sensing, Technical University Berlin, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
- Fraunhofer MEVIS, Bremen, Germany
| | - Kristian Hildebrand
- Department VI Computer Science and Media, Berlin University of Applied Sciences and Technology, Berlin, Germany
| | - Sebastian Pokutta
- Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Berlin, Germany
- Institute of Mathematics, Technical University Berlin, Berlin, Germany
| | - Kartikey Sharma
- Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Berlin, Germany
| | - Adam Hilbert
- CLAIM-Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Sobesky
- Johanna-Etienne-Hospital, Neuss, Germany
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ivana Galinovic
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ahmed A. Khalil
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Mind, Brain, Body Institute, Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany
| | - Jochen B. Fiebach
- Centre for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dietmar Frey
- CLAIM-Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
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Subramaniam P, Kossen T, Ritter K, Hennemuth A, Hildebrand K, Hilbert A, Sobesky J, Livne M, Galinovic I, Khalil AA, Fiebach JB, Frey D, Madai VI. Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks. Med Image Anal 2022; 78:102396. [DOI: 10.1016/j.media.2022.102396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/28/2022] [Accepted: 02/17/2022] [Indexed: 02/01/2023]
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Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
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Liu H, Peng C, Tian Y, Long S, Tian F, Wu Z. GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel. ENTROPY (BASEL, SWITZERLAND) 2022; 24:430. [PMID: 35327940 PMCID: PMC8953244 DOI: 10.3390/e24030430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/02/2022] [Accepted: 03/17/2022] [Indexed: 11/30/2022]
Abstract
The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoretic channel model. To this end, we systematically reviewed GDP and LDP from the perspective of the information-theoretic channel in this survey. First, we presented the privacy threat model under information-theoretic channel. Second, we described and compared the information-theoretic channel models of GDP and LDP. Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models. Finally, we discussed the open problems of GDP and LDP based on different types of information-theoretic channel models according to the above systematic review. Our main contribution provides a systematic survey of channel models, definitions, privacy-utility metrics, properties, and mechanisms for GDP and LDP from the perspective of information-theoretic channel and surveys the differential privacy synthetic data generation application using generative adversarial network and federated learning, respectively. Our work is helpful for systematically understanding the privacy threat model, definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP from the perspective of information-theoretic channel and promotes in-depth research and analysis of GDP and LDP based on different types of information-theoretic channel models.
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Affiliation(s)
- Hai Liu
- Guizhou Big Data Academy, Guizhou University, Guiyang 550025, China;
- College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (Y.T.); (S.L.)
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Changgen Peng
- Guizhou Big Data Academy, Guizhou University, Guiyang 550025, China;
- College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (Y.T.); (S.L.)
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Youliang Tian
- College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (Y.T.); (S.L.)
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Shigong Long
- College of Computer Science and Technology, Guizhou University, Guiyang 550025, China; (Y.T.); (S.L.)
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
| | - Feng Tian
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China; (F.T.); (Z.W.)
| | - Zhenqiang Wu
- School of Computer Science, Shaanxi Normal University, Xi’an 710119, China; (F.T.); (Z.W.)
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A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311191] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.
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Chang Y, Fang C, Sun W. A Blockchain-Based Federated Learning Method for Smart Healthcare. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:4376418. [PMID: 34868289 PMCID: PMC8635913 DOI: 10.1155/2021/4376418] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/07/2021] [Accepted: 11/10/2021] [Indexed: 11/18/2022]
Abstract
The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.
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Affiliation(s)
- Yuxia Chang
- Department of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou 450001, China
- Key Laboratory of Nursing Medicine of Henan Province, Zhengzhou 450001, China
- People's Hospital of Zhengzhou University, Zhengzhou 450001, China
| | - Chen Fang
- Information Engineering University, Zhengzhou 450001, China
| | - Wenzhuo Sun
- Department of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou 450001, China
- Key Laboratory of Nursing Medicine of Henan Province, Zhengzhou 450001, China
- People's Hospital of Zhengzhou University, Zhengzhou 450001, China
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Chang V, Goble C, Ramachandran M, Deborah LJ, Behringer R. Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2021; 23:1363-1367. [PMID: 34744495 PMCID: PMC8563356 DOI: 10.1007/s10796-021-10216-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
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