With the increasing adoption of lifts in daily life, the safe operation of the lift system is inevitable in modern lives. Enabled by our collaborator’s IoT platform with wireless connectivity for lifts, service providers and lift producers are able to collect and visualize the comprehensive runtime status data of the lifts on cloud in a large area with IoT nodes distributed to the lifts. However, this state-of-the-art IoT system naturally facing cybersecurity and privacy challenges due to the ubiquitous communication and data collection scheme. In this project, we propose an edge-cloud collaborative learning solution to improve the status monitoring as well as enabling effective
privacy preserving capability for it.