F2SOD: A Federated Few-Shot Object Detection

With the popularity of edge computation, object detection applications face challenges of limited data volume and data privacy. To address these, we propose a federated few-shot object detection framework, F2SOD. It involves three steps: collaborative base model training with base class data, novel data augmentation via an improved diffusion model, and collaborative base model fine-tuning for novel model using augmented data. Specifically, we present a data augmentation method based on diffusion models with a twofold-tag prompt construction and object location embedding. In addition, we present distributed framework for training base and novel models, where the base model integrates the Squeeze-and-Excitation attention mechanism into the feature re-weighting module. Experiments on public datasets demonstrate that F2SOD achieves efficient few-shot object detection, outperforming State-of-the-Art methods in both accuracy and efficiency.