  Rapidly developing intelligent healthcare systems are underpinned by Sixth
Generation (6G) connectivity, ubiquitous Internet of Things (IoT), and Deep
Learning (DL) techniques. This portends a future where 6G powers the Internet
of Medical Things (IoMT) with seamless, large-scale, and real-time connectivity
amongst 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 IoMT. We consider multiple hospitals with clusters of mixed
IoMT 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.
