Privacy-Centric Approach in Leveraging Federated Learning for Improved Parkinson's Disease Diagnosis

Privacy-Centric Approach in Leveraging Federated Learning for Improved Parkinson's Disease Diagnosis

DOI: 10.4018/979-8-3693-2639-8.ch009
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Parkinson's disease (PD), a neurodegenerative disorder that affects millions of people worldwide, requires an accurate and early diagnosis for opportune interventions and individualized treatment. However, the process of diagnosis frequently necessitates access to extensive patient data, which raises significant privacy concerns. Federated machine learning (FedML) techniques provide an enticing solution by permitting model training across multiple data sources without sharing raw data, thereby mitigating privacy risks. In the context of Parkinson's disease diagnosis, this chapter provides a comprehensive review in the field of medical diagnosis by highlighting the potential of FedML strategies in improving PD diagnosis with the use of various FedML techniques. Also, the chapter offers a novel approach which utilizes the strengths of FedML techniques. By distributing the training process and leveraging FedML techniques, the authors propose an approach which harnesses a diverse range of patient data and enables accurate PD diagnosis without compromising patient privacy.
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1. Introduction

The prevalence of Parkinson's disease (PD) has experienced a twofold increase over the course of the last quarter-century(Marras et al., 2018). Global estimates in 2019 put Parkinson's disease cases exceeding over 8.5 million. Recent estimates show that Parkinson's disease (PD) caused 5.8 million disability-adjusted life years (DALYs) in 2019, an 81% increase from 2000. Additionally, PD was responsible for causing 329,000 deaths, indicating a rise of over 100% since the year 2000. More than 10 million people (about half the population of New York) worldwide suffer with PD.

This chapter explores the potential of Federated Machine Learning (FedML) in the context of Parkinson's Disease (PD) diagnosis in depth. The first step is to investigate the fundamental challenges and potential gains associated with medical diagnosis data management. When dealing with extensive and sensitive patient data that are necessary for an accurate diagnosis, protecting their privacy and data security is of the utmost importance.

The primary perspective of this chapter demonstrates the critical need to strike an accurate balance between achieving precise and timely PD diagnosis, while upholding the principles of patient privacy. An essential part of this study is making sure that advancements in medical diagnosis do not compromise personal information.

This chapter aims to validate the significance of privacy in the sharing of healthcare data and explore the potential of Federated Machine Learning (FedML) as a solution for preserving privacy. The insights presented in this chapter are derived from a comprehensive review of existing literature.

The study aims to examine the complex nature of data privacy, and the ever-evolving regulatory framework that governs healthcare data. Recognizing the limitations and risks associated with traditional data-sharing practices, advocates for innovative, privacy-centric approaches.

Furthermore, this chapter provides a detailed examination of the present state of PD diagnosis. Here, four distinct FedML strategies: Federated Averaging (FedAvg), Federated Adam (FedAdam), Federated Fault Tolerant (FedFaultTolerant), and Federated Adagrad (FedAdagrad) are compared and contrasted. This examination aims to provide a comprehensive understanding of how these FedML strategies stand to revolutionize the diagnostic landscape by enhancing accuracy and efficiency while mitigating privacy risks.

The main goals of this chapter can be summarized into five distinct objectives:

  • a.

    Highlight the Significance of Accurate and Timely PD Diagnosis: Underscore the importance of early PD diagnosis in the context of patient care.

  • b.

    Emphasize Ethical and Privacy Concerns: Shed light on the ethical and privacy concerns tied to the use of patient data in medical diagnosis.

  • c.

    Introduction of FedML as a Privacy-Preserving Solution: Federated Machine Learning (FedML) is a viable and ethically responsible approach to address the aforementioned concerns.

  • d.

    Compare and contrast the Potential of Four FedML Strategies: Assess the efficacy of FedAvg, FedAdam, FedFaultTolerant, and FedAdagrad in enhancing PD diagnosis accuracy.

  • e.

    Provide a Balanced Perspective: Offer a comprehensive view of the challenges, controversies, and opportunities surrounding the integration of FedML in healthcare, particularly in the context of PD diagnosis.

Through its comprehensive analysis and comparison of FedML strategies, this chapter aims to make a significant contribution to the ongoing discussion about privacy, medical diagnosis, and innovative machine learning techniques.


2. Literature Review

The healthcare domain has witnessed remarkable developments driven by advanced technologies. This literature review aims to explore significant research papers that shed light on the innovative utilization of machine learning, federated learning, and data-driven methodologies in the realm of Parkinson's disease diagnosis. These studies not only show the potential of these technologies but also point out significant drawbacks and challenges that must be considered when implementing solutions for improved Parkinson's disease diagnosis.

Key Terms in this Chapter

Privacy Preservation: Techniques and strategies to protect sensitive data while still extracting meaningful insights.

Feature Selection: The process of choosing a subset of relevant attributes or features from a larger dataset to improve model performance and interpretability.

Correlation Analysis: Statistical technique to measure the strength and direction of a linear relationship between two variables.

Accuracy: A metric that measures the ratio of correctly predicted instances to the total instances in a classification problem.

Aggregation Strategies: Methods for combining local model updates in federated learning to construct a global model.

Deep Neural Network (DNN): A type of artificial neural network with multiple layers between the input and output layers, capable of learning complex representations from data.

Clinical Validation: The process of rigorously testing and validating a medical or diagnostic model in real-world clinical settings.

Parkinson's Disease (PD): A neurodegenerative disorder that affects movement, characterized by tremors, rigidity, and bradykinesia.

Federated Learning (FedML): A machine learning approach that trains models collaboratively across decentralized devices while keeping data localized, enhancing privacy.

Scalability: The ability of a system or algorithm to handle larger datasets, more clients, or increased computational demands without compromising performance.

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