Horizontal Federated Computer Vision
This addresses privacy-preserving model training for organizations handling distributed visual data, but it is incremental as it applies existing federated learning to standard computer vision tasks.
The paper tackled the problem of training computer vision models on decentralized visual data while complying with privacy regulations, by implementing federated versions of Faster R-CNN and Fully Convolutional Networks, achieving results on COCO2017 and CamVid datasets.
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.