Federated transfer learning enabled smart work packaging for preserving personal image information of construction worker

Abstract

The rapidly expanding number of IoT-based camera devices makes smart work packaging (SWP) easier to access massive construction workers' personal image information for occupational health and safety (OHS) status monitoring. SWP can then transmit these personal data to the cloud for training the machine learning models and offer safety alerts or health insights. However, there are two urgently important challenges. Firstly, the machine learning model needs to aggregate the SWPs' image data from each construction worker, which may pose a risk to private data leakage without strict privacy and security agreement. In addition, the machine learning models trained on all SWPs' image data may compromise the personalization of image-based OHS status monitoring for each construction worker. To address the above issues, this study proposes a FedSWP framework, the federated transfer learning-enabled SWP for protecting the personal image information of construction workers in OHS management. FedSWP executes the gradient parameters aggregation through federated learning for the image data in each SWP and builds relatively personalized models by transfer learning. Crane operators' facial fatigue monitoring experiments are conducted and have evaluated that FedSWP can achieve accurate and personalized safety alerts and healthcare. This study paves the way for the generalization and extension of FedSWP in many construction OHS applications.