Federated Learning Using Knowledge Distillation for CNN-Based eHealth Data Analysis

The chapter delves into the transformative potential of federated learning and knowledge distillation in the realm of eHealth data analysis, particularly for Human Activity Recognition (HAR). It addresses the critical challenges posed by traditional machine learning approaches, such as privacy, security, and latency, especially in IoT environments where data is sensitive and geographically dispersed. The proposed MD_CNN model leverages federated learning to enable collaborative model training without compromising data privacy, ensuring that sensitive information remains local. This approach not only enhances data security but also improves model performance through knowledge distillation, allowing smaller, efficient models to mimic the capabilities of larger, more complex ones. The chapter provides a detailed examination of the MD_CNN model's architecture, training process, and performance metrics, demonstrating its superiority over existing methods. It also explores the application of convolutional neural networks (CNNs) in analyzing medical images and detecting complex patterns, highlighting their role in rapid and accurate diagnoses. The experimental results showcase the MD_CNN model's impressive accuracy, precision, recall, and AUC, surpassing state-of-the-art performance on the HAR dataset. The chapter concludes with a discussion on future directions, suggesting the extension of the MD_CNN model to other complex datasets to validate its robustness and effectiveness in various scenarios of human activity recognition.