Demo: Addressing Inter-Intra Patient Variability via Personalized Meta-Federated Learning in IoT-Enabled Health Monitoring

Federated learning (FL) has been widely adopted in IoT-enabled health monitoring on biosignals. However, the global model may not adapt well to each target patient's data due to complex biosignals' morphological characteristics caused by inter- and intra-patient variability. To address the challenge, we propose a personalized meta-federated learning framework (PMFed) for patient-specific health monitoring in IoT. Experimentally, we evaluate the effectiveness and generalization of PMFed over three health monitoring tasks on a physical IoT platform. Experimental results show that the PMFed excels at empirical performances when compared with SOTA personalized FL algorithms.