Horizontal Federated Computer Vision
Paul K. Mandal Cole Leo Connor Hurley
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.