FtlSPG: A Federated Transfer Learning Framework for Personalized Safety Protective Gear Detection in Electric Power Industry

Safety protective gear (SPG) detection based on the machine learning model plays an important role in improving outdoor personnel safety in the electric power industry. However, the detection method of transmitting video to the cloud faces a series of challenges, such as privacy disclosure and high latency. To solve this problem, we present FtlSPG, a federated transfer learning framework for SPG detection. In particular, under the three-layer pyramid architecture of 'site-companyCloud-globalServer,' we propose a federated personalized model based on local batch normalization and dynamical weighting for the source domain with labeled video. Moreover, a federated domain adaptation model based on a federated deep adversarial network and model self-training is presented for the target domain with unlabeled video. Finally, we verify the effectiveness of FtlSPG in real-world power companies. Extensive experiments demonstrate that FtlSPG can significantly outperform existing schemes, in terms of privacy protection, detection precision, and response latency.