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.

翻译：由第六代（6G）通信技术、普适物联网和深度学习技术支撑的智能医疗系统正在快速发展。这预示着6G将赋能医疗物联网，实现实体间无缝、大规模、实时的互联。本文提出一种基于卷积神经网络的联邦学习框架，该框架结合了基于安全多方计算的聚合与加密推理方法，并应用于6G与医疗物联网场景中。我们考虑多所医院部署混合医疗物联网及边缘设备集群，对本地训练的模型进行加密。随后，各医院将加密后的本地模型发送至云端进行基于SMPC的加密聚合，生成加密全局模型。最终，加密全局模型返回至各边缘服务器以开展更本地化的训练，进一步提升模型精度。此外，医院可在其边缘服务器或云端执行加密推理，同时保障数据与模型隐私。通过采用不同CNN模型与数据集进行多项实验，评估了所提框架的性能。