Adaptive Federated Learning Framework for Privacy-Preserving Consumer-Centric IoMT: A Novel Secure Data Collaboration Model

With the increasing adoption of Internet of Medical Things (IoMT) devices, modern healthcare systems face persistent challenges related to data privacy, device heterogeneity, communication overhead, and scalability especially in consumer electronics environments. This paper hypothesizes that a hierarchical and privacy-preserving federated learning architecture can address these challenges and enable efficient, secure, and personalized healthcare insights across distributed IoMT networks. To validate this, we present the Adaptive Federated Learning Framework (AFLF), a novel framework tailored for resource-constrained consumer devices such as wearable ECG monitors, glucose trackers, and portable neuro-sensors. AFLF incorporates four core components: (1) a hierarchical edge–fog–cloud model for scalable and latency-aware training, (2) a Secure Data Collaboration Protocol (SDCP) that combines blockchain-based audit trails with differential privacy to ensure integrity and confidentiality, (3) the Adaptive Personalized Federated Learning Algorithm (APFLA) that dynamically tunes learning rates and model weights for personalized inference, and (4) a ternary threshold-based gradient compression technique that reduces communication overhead by 45.3%. The framework is deployed on Raspberry Pi 4 (edge) and NVIDIA Jetson AGX Xavier (fog) platforms, using PyTorch Mobile for implementation. Experimental validation using four real-world datasets, CardioFit (ECG), GlucoWatch (glucose), PulseOx (PPG), and NeuroMotion (EEG), demonstrates that AFLF improves model accuracy by up to 12% reduces training time by 38% and preserves user privacy with a differential privacy budget of ε = 2.1. These results confirm that AFLF offers a robust and scalable privacy-preserving federated learning solution for next-generation consumer-centric smart healthcare applications.