Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor

Ngo, Thang; Nguyen, Dinh C.; Pathirana, Pubudu N.; Corben, Louise A.; Delatycki, Martin B.; Horne, Malcolm; Szmulewicz, David J.; Roberts, Melissa M.

doi:10.1109/tnsre.2022.3161272

Cited by 11 publications

(5 citation statements)

References 39 publications

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“…T ρ+T η T ρ+T η+F ρ+F η * 100 [10], [38], [13], [36], [58], [46], [47], [28], [77], [35], [7], [79], [64], [29], [1], [85], [9], [53], [25], [51], [66], [17], [45], [50] Sensitivity (Recall)…”

Section: Measures Equation References Accuracymentioning

confidence: 99%

Role of federated learning in healthcare systems: A survey

Rana¹,

Marwaha²

2024

MFC

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Nowadays, machine learning affects practically every industry, but the effectiveness of these systems depends on the accessibility of training data sets. Every device now produces data, and that data can serve as the foundation for upcoming technologies. Traditional machine learning systems need centralised data for their training, but the availability of valid and good amounts of data is not always possible due to various privacy risks. But federated learning can solve this issue [78]. In a federated learning (FL) environment, a model can be trained on decentralised datasets by involving a large number of participants, such as mobile devices or entire enterprises. Researchers are using this technique in various fields and getting great responses. The importance of using federated learning in the healthcare industry is highlighted in this paper since there is a wealth of data available in hospitals or electronic health records that may be used to train medical systems but cannot be shared due to privacy issues. The main contribution of this paper is to highlight the role of federated learning in the medical field. It also presents a list of frameworks available to implement federated learning models. The paper also listed the evaluation metrics used to check the efficiency of a federated learning model. Broadly used evaluation metrics are accuracy, precision, recall, and F1-score. Open issues for research in this area are also discussed at the end of this paper.

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Section: Measures Equation References Accuracymentioning

confidence: 99%

Role of federated learning in healthcare systems: A survey

Rana¹,

Marwaha²

2024

MFC

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“…According to research [64], a lightweight convolutional architecture (MobileNetV2) with a recurrence plot transformer could successfully identify 86.69% of cases for Cerebellar Ataxia. The installation of DL-based FL algorithms resulted in a reduction in the amount of money that the service provider spent on operational expenses [65].…”

Section: ) Cerebellar Ataxia Detectionmentioning

confidence: 99%

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

Abbas,

Ahmad,

Syed

et al. 2024

IEEE Access

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With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT.

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“…Univariate analysis: Lateral velocity of COM were higher in CA individuals than healthy controls. IMU wearable sensor (2022) [8], [34], [56], [103]- [105] Wearable sensors IMU capture ataxic balance.…”

Section: Optical Tracking Camera-based System (2009 -2016) [99] [102]mentioning

confidence: 99%

“…For instance, research in PD showed the high frequency (3 Hz to 8 Hz) components of leg movements during Freezing of Gait (FOG) were not present in ordinary walking or standing. By calculating a power ratio of the "freeze" band [3][4][5][6][7][8] to the "locomotor" band [0.5-3 Hz], the authors could estimate a threshold to identify FOG events [194]. With a similar method, Handojoseno et al proved the power spectral density and wavelet energy of electroencephalography (EEG) could function as promising biomarkers to indicate FOG with 80% accuracy [195].…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

“…It, therefore, presents an opportunity for an engineering approach to build a system that recognises the presence and severity of ataxia. In the interest of reducing the gap between clinical need and engineering endeavor, research groups have been using machine learning (ML) technology to advance the development of clinically useful objective measures of CA [8]- [11]. The main aims of this paper to this end are:…”

Section: Introductionmentioning

confidence: 99%

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Technological Evolution in the Instrumentation of Ataxia Severity Measurement

et al. 2023

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Cerebellar ataxia is the poorly coordinated movement that results from injury or disease affecting the cerebellum. The diagnosis and assessment of ataxia are significantly challenging due to dependency on clinicians' experience and the attendant subjectivity of such a process. In recent years, neuroimaging and sensor-based approaches, supported by effective machine learning techniques have made advances in the pursuit of addressing these clinical challenges. In this work, we present an outline of approaches to applying machine learning to this clinical challenge. We first provide a fundamental clinical overview with practical problems and then from a machine learning perspective, outline possible approaches with which to address these clinical challenges. Also discussed are the limitations in existing methods, the provision of cross disciplinary approaches and the current state-of-the-art as a potential basis for future research.

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Federated Deep Learning for the Diagnosis of Cerebellar Ataxia: Privacy Preservation and Auto-Crafted Feature Extractor

Cited by 11 publications

References 39 publications

Role of federated learning in healthcare systems: A survey

Role of federated learning in healthcare systems: A survey

Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture

Technological Evolution in the Instrumentation of Ataxia Severity Measurement

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