mlsanityData to 7 Oct 2026

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

arXiv 2111.11066 ↗First posted 22 Nov 2021cs.CV cs.AI cs.LGcited by 32

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Abstract (arXiv)

Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 73 of its 89 references that are in the map. First posted on arXiv 22 Nov 2021.

Builds on

73 of 89 references.

References · 73

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  8. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  9. 2019Advances and Open Problems in Federated Learningcited by 3,021
  10. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  11. 2019Searching for MobileNetV3cited by 1,984
  12. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
38 more
  1. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  2. 2020Adaptive Federated Optimizationcited by 1,036
  3. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  5. 2018How To Backdoor Federated Learningcited by 745
  6. 2018AutoAugment: Learning Augmentation Policies from Datacited by 728
  7. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  8. 2020Federated Learning with Matched Averagingcited by 564
  9. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  10. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  11. 2019Agnostic Federated Learningcited by 451
  12. 2019Federated Machine Learning: Concept and Applicationscited by 409
  13. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  14. 2019Fair Resource Allocation in Federated Learningcited by 389
  15. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  16. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  17. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  18. 2021A Field Guide to Federated Optimizationcited by 312
  19. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  20. 2020Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrievalcited by 305
  21. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  22. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  23. 2018Distributed learning of deep neural network over multiple agentscited by 277
  24. 2019Can You Really Backdoor Federated Learning?cited by 250
  25. 2018Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learningcited by 242
  26. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  27. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  28. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 178
  29. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  30. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  31. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  32. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  33. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  34. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  35. 2018A generic framework for privacy preserving deep learningcited by 103
  36. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  37. 2020Acceleration for Compressed Gradient Descent in Distributed and Federated Optimizationcited by 90
  38. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78

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Citing papers per year: 2022: 10; 2023: 10; 2024: 6; 2025: 5; 2026: 1.

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  1. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  2. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  3. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  4. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
  5. 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyondcited by 37
  6. 2022On the Convergence of Clustered Federated Learningcited by 31
  7. 2022Personalized Federated Learning With Graphcited by 29
  8. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  9. 2022FLAIR: Federated Learning Annotated Image Repositorycited by 22
  10. 2023Federated Learning for Computer Visioncited by 22
  11. 2024FedImpro: Measuring and Improving Client Update in Federated Learningcited by 15
  12. 2022Differentially Private CutMix for Split Learning with Vision Transformercited by 13
20 more
  1. 2024FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusioncited by 11
  2. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
  3. 2022FLAME: Federated Learning Across Multi-device Environmentscited by 8
  4. 2023FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Thingscited by 8
  5. 2023Handling Data Heterogeneity via Architectural Design for Federated Visual Recognitioncited by 8
  6. 2023Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settingscited by 7
  7. 2024MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classescited by 7
  8. 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
  9. 2023Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beatcited by 4
  10. 2023Federated Object Detection for Quality Inspection in Shared Productioncited by 3
  11. 2025Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributionscited by 3
  12. 2025Equitable Federated Learning with NCAcited by 3
  13. 2024Split Learning in Computer Vision for Semantic Segmentation Delay Minimizationcited by 2
  14. 2025Personalized Federated Learning via Learning Dynamic Graphscited by 2
  15. 2023ASU-CNN: An Efficient Deep Architecture for Image Classification and Feature Visualizationscited by 1
  16. 2023Unlocking the Potential of Federated Learning for Deeper Modelscited by 1
  17. 2025Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluationcited by 1
  18. 2026Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancementcited by 1
  19. 2024FedEGG: Federated Learning with Explicit Global Guidancecited by 0
  20. 2025AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AIcited by 0