Federated Edge Intelligence for Enhanced Security in Consumer Intermittent Healthcare Devices Using Adversarial Examples

Modern consumer electronics that integrate the concept of connected personal medical devices are known as Smart Healthcare Systems (SHS). The SHS utilized healthcare devices and edge computing for data capture, transmission via smart devices, analysis, and the delivery of healthcare services. Cyberattacks on consumer medical devices in the healthcare sector are becoming increasingly common. Technological frameworks like edge computing can act as an intermediary layer between the cloud and SHS, reducing the burden and enhancing data security and privacy. In this study, we proposed a novel edge intelligence approach for improving medical device security that employs Federated Learning (FL) and network traffic adversarial examples. First, we generated a distinctive dataset using image-based features extracted from network traffic bytes. Secondly, due to the intermittent behavior of clients, the dataset received from each client may be imbalanced, which can negatively affect performance. Third, adversarial examples are generated to assess the robustness of datasets, utilizing four distinct types of adversarial attack methods that introduce perturbations into the input data. Finally, the cooperative FL architecture ensured data security, privacy, and edge intelligence. The proposed method is analyzed using two standard datasets, CICIoMT2024 and Edge-IIoT, achieving 97.45% and 96.7% detection accuracy, respectively.