Federated Object Detection

Federated object detection aims to train accurate object detection models across multiple decentralized datasets without compromising data privacy, a crucial aspect for sensitive visual data. Current research focuses on addressing challenges like data heterogeneity and limited labeled data, employing techniques such as federated averaging, meta-learning, and adaptive inter-class representation learning within architectures like Faster R-CNN and YOLOv5. This field is significant for enabling collaborative model training in privacy-sensitive applications such as autonomous driving and quality inspection, bridging the gap between the performance of centralized and decentralized training approaches.