In the modern world, the amount of visual data recorded has been rapidly increasing. In many cases, data is stored in geographically distinct locations and thus requires a large amount of time and space to consolidate. Sometimes, there are also regulations for privacy protection which prevent data consolidation. In this work, we present federated implementations for object detection and recognition using a federated Faster R-CNN (FRCNN) and image segmentation using a federated Fully Convolutional Network (FCN). Our FRCNN was trained on 5000 examples of the COCO2017 dataset while our FCN was trained on the entire train set of the CamVid dataset. The proposed federated models address the challenges posed by the increasing volume and decentralized nature of visual data, offering efficient solutions in compliance with privacy regulations.

翻译：在现代世界中，视觉数据的记录量正在迅速增长。在许多情况下，数据存储在地理位置分散的不同地点，因此需要大量的时间和空间进行整合。有时，还存在隐私保护法规，阻止数据整合。在本工作中，我们提出了用于目标检测与识别的联邦Faster R-CNN（FRCNN）实现，以及用于图像分割的联邦全卷积网络（FCN）实现。我们的FRCNN在COCO2017数据集的5000个样本上进行训练，而我们的FCN则在CamVid数据集的完整训练集上进行训练。所提出的联邦模型应对了视觉数据量增长和分散性带来的挑战，提供了符合隐私法规的高效解决方案。