查看文章

Recently, Internet of Things (IoT) systems in the network edge with embedded intelligence emerged as a trending research topic. Edge computing offers a significant advantage over the traditional form of sharing personal data with a centralized entity since the latter paradigm may affect the user’s privacy, e.g., due to explicit exchange of sensitive biomedical data. To address this inherent data privacy issue, in this paper, we focus on designing an asynchronously weight updating federated learning algorithm toward the much anticipated AI-on-Edge IoT systems. Among numerous use-cases, we consider the face mask detection problem, which is traditionally considered as a centralized computer vision task. We take a different approach to distribute the learning tasks to the users in a federated learning framework, and then investigate the performance trade-off between synchronous and asynchronously weight …