mlsanityData to 7 Oct 2026

FedVision: An Online Visual Object Detection Platform Powered by Federated Learning

arXiv 2001.06202 ↗First posted 17 Jan 2020cs.LG cs.CV stat.MLcited by 121

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Abstract (arXiv)

Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to privacy concerns and the high cost of transmitting video data, it is highly challenging to build object detection models on centrally stored large training datasets following the current approach. Federated learning (FL) is a promising approach to resolve this challenge. Nevertheless, there currently lacks an easy to use tool to enable computer vision application developers who are not experts in federated learning to conveniently leverage this technology and apply it in their systems. In this paper, we report FedVision - a machine learning engineering platform to support the development of federated learning powered computer vision applications. The platform has been deployed through a collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Over four months of usage, it has achieved significant efficiency improvement and cost reduction while removing the need to transmit sensitive data for three major corporate customers. To the best of our knowledge, this is the first real application of FL in computer vision-based tasks.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jan 2020.

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Citing papers per year: 2021: 21; 2022: 26; 2023: 32; 2024: 22; 2025: 13; 2026: 7.

121 papers.

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  1. 2021Model-Contrastive Federated Learningcited by 762
  2. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  3. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  4. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  5. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  6. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  7. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  8. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  9. 2022RSCFed: Random Sampling Consensus Federated Semi-supervised Learningcited by 32
  10. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  11. 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
  12. 2022Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learningcited by 29
38 more
  1. 2022FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Modelcited by 25
  2. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  3. 2023Federated Learning for Computer Visioncited by 22
  4. 2022A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperationcited by 21
  5. 2022Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical Worldcited by 20
  6. 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
  7. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  8. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  9. 2022FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learningcited by 17
  10. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  11. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  12. 2024Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baselinecited by 17
  13. 2022FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregationcited by 16
  14. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  15. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  16. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networkscited by 15
  17. 2023FedBone: Towards Large-Scale Federated Multi-Task Learningcited by 14
  18. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  19. 2024Deep Efficient Private Neighbor Generation for Subgraph Federated Learningcited by 14
  20. 2022Differentially Private CutMix for Split Learning with Vision Transformercited by 13
  21. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  22. 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
  23. 2022Application of federated learning in manufacturingcited by 11
  24. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
  25. 2022FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacycited by 9
  26. 2023Federated Recommendation with Additive Personalizationcited by 9
  27. 2023BadVFL: Backdoor Attacks in Vertical Federated Learningcited by 9
  28. 2023pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharingcited by 9
  29. 2023A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspectivecited by 9
  30. 2024FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clusteringcited by 9
  31. 2021FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Searchcited by 8
  32. 2021From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimizationcited by 8
  33. 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8
  34. 2023Edge-Based Video Analytics: A Surveycited by 8
  35. 2025PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learningcited by 8
  36. 2022Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learningcited by 7
  37. 2022Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Reviewcited by 7
  38. 2023Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learningcited by 7

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