Cloud-Edge-End Collaboration Personalized Semi-supervised Federated Learning for Visual Localization
Qixiang Ma, Zhe Zhang, Zhenhan Zhu, Yanchao Zhao · 2023
Deep learning-based visual localization methods use convolutional neural networks to directly regress the position of a target. However, previous studies only consider localization in a single scene and neglect personalized localization in multiple scenes. Furthermore, changes in the scene result in a reduced accuracy due to the model’s lack of adaptability. Moreover, traditional centralized training methods pose data privacy concerns. In this paper, we propose a personalized semi-supervised federated learning framework with cloud-edge-end collaboration, called FedVL. The hierarchical architecture extends single-scene localization to multiple scenes, while the federated learning mechanism ensures data privacy. In this framework, we apply personalized federated learning to achieve scene-specific model and employ semi-supervised federated learning to allow the localization model to adapt to scene changes. Experiments conducted on indoor and outdoor datasets demonstrate the effectiveness of this approach.