Energy Aware Federated Learning with Application of Activity Recognition

Human activity recognition (HAR) based on smart devices is gaining increasing attention from the pervasive computing research community due to its wide application in smart healthcare. Modern deep learning models for recognizing human activities rely heavily on sensor data to attain great accuracy. However, leveraging data gathered from smart devices to train such models in a data center results in significant energy consumption and potential privacy violations. By merging numerous local models that are trained on data coming from various clients, federated learning can be used to address the aforementioned problems. By creating a federated learning-based convolutional neural network (CNN) model, which is trained on both public and real-world datasets, we analyze federated learning (FL) to train a human activity identification classifier and compare its performance to centralized learning. The global model achieves accuracy comparable to centralized learning when trained using federated learning on skewed datasets. Additionally, we discover that the selection of clients is a significant problem and suggest a federated learning algorithm that selects the maximum number of clients to increase the convergence rate and model correctness of FL.