Enhancing Communication-Efficient Federated Learning for Human Activity Recognition Through Knowledge Distillation

Human Activity Recognition (HAR) has profound applications in domains such as healthcare, wearable devices, and smart environments, where continuous monitoring is essential. However, traditional centralized learning approaches raise privacy concerns and are infeasible for resource-constrained devices due to high communication costs. Federated Learning (FL) offers a privacy-preserving solution by training models locally and aggregating updates, but it still encounters considerable communication overhead. This paper proposes an optimized FL framework that integrates model compression through Knowledge Distillation (KD) to reduce communication costs while preserving model performance. Using a Teacher-Student model architecture, the framework enables the deployment of highly compressed Student models without compromising accuracy or other performance metrics. Experimental evaluations on a smartphone-based HAR dataset show that the proposed framework achieves comparable accuracy, precision, and recall w.r.t. the original (uncompressed) models, with up to 75% reduction in model size (and, consequently, communication cost). This approach demonstrates scalability and feasibility for real-world HAR applications on resource-constrained devices, also laying the groundwork for efficient, privacy-preserving distributed learning in heterogeneous computational environments.