mlsanityData to 7 Oct 2026

Horizontal Federated Computer Vision

arXiv 2401.00390 ↗First posted 31 Dec 2023cs.CV cs.AI cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 31 Dec 2023.

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23 of 43 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2020Image Segmentation Using Deep Learning: A Surveycited by 659
  7. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  8. 2018Object Detection with Deep Learning: A Reviewcited by 524
  9. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  10. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  11. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  12. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
11 more
  1. 2023FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Modelscited by 62
  2. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  3. 2022Architecture Agnostic Federated Learning for Neural Networkscited by 30
  4. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  5. 2022How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?cited by 19
  6. 2015Places205-VGGNet Models for Scene Recognitioncited by 13
  7. 20172D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentationcited by 10
  8. 2022Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detectioncited by 10
  9. 2022Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphonescited by 6
  10. 2020Towards Compliant Data Management Systems for Healthcare MLcited by 3
  11. 2023Fairness and Privacy in Federated Learning and Their Implications in Healthcarecited by 3

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