SecureFogDL: a federated Transformer framework for secure and intelligent fog computing in healthcare IoT

Patient care has been transformed with the increasing adoption of Healthcare IoT (H-IoT) systems, allowing for continuous monitoring and personalised treatment. The connected medical devices pose some significant challenges for security and privacy because such systems produce sensitive personal data in large quantities. To address the above challenges, SecureFogDL, a federated BERT-based Transformer classifier framework to improve security, privacy, and performance for healthcare IoT on top of fog computing, is proposed in this paper. This framework uses federated learning techniques to keep sensitive data decentralised and private, while at the same time applying a BERT-based BERT-based Transformer classifier classifier to identify kinds of attacks correctly and mitigate them, such as DDoS attacks. Autoencoders are applied for feature extraction while reducing the complexity of IoT traffic, in favour of better performance of models on fog nodes with limited resources. SecureFogDL presents a promote scalable and preserves privacy solution for attack detection and decision-making within healthcare IoT environments.