SMPC-Based Federated Learning for 6G-Enabled Internet of Medical Things

Rapidly developing intelligent healthcare systems are underpinned by sixth generation (6G) connectivity, the ubiquitous Internet of Things, and deep learning (DL) techniques. This portends a future where 6G powers the Internet of Medical Things (loMT) with seamless, large-scale, and real-time connectivity among entities. This article proposes a convolutional neural network (CNN)-based federated learning framework that combines secure multi-party computation (SMPC) based aggregation and Encrypted Inference methods, all within the context of 6G and 1oMT. We consider multiple hospitals with clusters of mixed 1oMT and edge devices that encrypt locally trained models. Subsequently, each hospital sends the encrypted local models for SMPC-based encrypted aggregation in the cloud, which generates the encrypted global model. Ultimately, the encrypted global model is returned to each edge server for more localized training, further improving model accuracy. Moreover, hospitals can perform encrypted inference on their edge servers or the cloud while maintaining data and model privacy. Multiple experiments were conducted with varying CNN models and datasets to evaluate the proposed framework's performance.