Privacy-Preserving Fall Detection using Federated Machine Learning in IoT-based Applications
Falls are one of the leading causes of injury and death among the elderly, creating a growing need for efficient and privacy-preserving fall detection systems. Traditional machine learning approaches for fall detection often rely on centralized data collection, where sensitive user data is collected and stored on a central server. This poses significant privacy risks, especially in the healthcare
