Personalized Model Partitioning and Aggregation for Federated Human Activity Recognition

Federated learning-based human activity recognition (FL-HAR) has emerged as a significant research topic due to its extensive applications, enabling the collective integration of local client data and knowledge without compromising user privacy. A major challenge within FL-HAR is addressing the heterogeneity of data across distributed clients, which leads to varying feature distributions for the same activity across different clients. To overcome this limitation, we propose a novel algorithm for FL-HAR, termed FedPMP, with Adaptive Model Partitioning and Personalized Model Aggregation. This approach customizes individual models for each client, aligned with their individual data distribution. Specifically, our partitioning strategy categorizes the model into globally shared components and locally personalized components, based on layer-wise feature drift. The global components capture general features across the majority of user groups, while the local components extract user-specific personalized features. Furthermore, our personalized model aggregation approach separately consolidates the shared and personalized components, facilitating collaboration among clients in accordance with feature relevance. Notably, we prove that FedPMP converges under feature drift conditions, providing a solid theoretical foundation for its practical deployment. Extensive experiments on various real-world HAR benchmarks demonstrate that FedPMP can effectively address the issue of feature drift in HAR, boosting recognition accuracy for clients and reducing the variance in accuracy among them. Our method enhances recognition accuracy by an average factor of 5%, proving its efficacy and robustness under feature drift conditions.