Horizontal Federated Computer Vision

TL;DR

This work presents federated implementations for object detection and recognition using a federated Faster R-CNN (FRCNN) and image segmentation using a federated Fully Convolutional Network (FCN).

Abstract

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.

Figures (8)

• Figure 1: Data partitioning of horizontal and vertical federated learning
• Figure 2: Architecture of an RCNN, as proposed in 7112511
• Figure 3: Architecture of an FCN, as proposed in long2015fully
• Figure 4: Left: Source Image | Right: Semantic Mask
• Figure 5: IoU Calculation