Collaborative Governance: Blockchain-Based Federated Learning for Construction Safety Service
Xing Pan, Lieyun Ding, Botao Zhong, Luoxin Shen, Yuhang Wang · IEEE Transactions on Engineering Management · 2025
Deep learning (DL) models are increasingly used to identify unsafe activities for the construction safety service (CSS). However, two typical issues threaten DL training process performance: poor security and privacy in data sharing. To address these problems, a blockchain-based federated learning (BCFL) framework is proposed from a collaborative governance perspective to obtain optimistic DL models for CSS. Two special works of this BCFL framework are that 1) it develops the federated learning empowered privacy-preserving data sharing with the principle of sharing data model weights instead of raw data (especially for one DL application for CSS, the Fed-YOLOv4 model for workers’ unsafe behavior identification task is developed) and 2) it explores two blockchain-based secure model sharing strategies that involve blockchain–interplanetary file system combination and model training contribution computing. Then, the smart contracts are further developed with the strategies above to streamline the workflow of federated DL model training. Finally, we apply the proposed framework to the practical subway project. The results demonstrate that the proposed framework can improve the DL model training and acquire global DL models for CSS with good accuracy. Our findings indicate that great data security and privacy ensured by introducing blockchain and federated learning can optimize data sharing and then DL models can be improved. Moreover, this study provides managers with a collaborative governance perspective on how DL models can be improved and further applied to CSS. This enables managers to quickly understand project safety performance and make timely managerial decisions for construction management.