What is Privacy Preservation

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.