mlsanityData to 7 Oct 2026

Federated Learning for Computer Vision

arXiv 2308.13558 ↗First posted 24 Aug 2023cs.CVcited by 22

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Abstract (arXiv)

Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associated with storing and processing data on central cloud servers put these algorithms under severe strain. To address these issues, federated learning (FL) has emerged as a promising solution, allowing privacy preservation by training models locally and exchanging them to improve overall performance. Additionally, the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents, to the best of the authors' knowledge, the first review discussing recent advancements of FL in CV applications, comparing them to conventional centralized training paradigms. It provides an overview of current FL applications in various CV tasks, emphasizing the advantages of FL and the challenges of implementing it in CV. To facilitate this, the paper proposes a taxonomy of FL techniques in CV, outlining their applications and security threats. It also discusses privacy concerns related to implementing blockchain in FL schemes for CV tasks and summarizes existing privacy preservation methods. Moving on, the paper identifies open research challenges and potential future research directions to further exploit the potential of FL and blockchain in CV applications.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 79 of its 172 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

79 of 172 references.

References · 79

  1. 2017Federated Multi-Task Learningcited by 669
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  3. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  4. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  5. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  6. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  7. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  8. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
  9. 2018Distributed learning of deep neural network over multiple agentscited by 277
  10. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  11. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  12. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
38 more
  1. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  2. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 159
  3. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  4. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  5. 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
  6. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  7. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  8. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  9. 2022Divergence-aware Federated Self-Supervised Learningcited by 71
  10. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
  11. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 62
  12. 2019Federated Learning with Bayesian Differential Privacycited by 60
  13. 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
  14. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  15. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  16. 2020Federated Learning in Vehicular Networkscited by 53
  17. 2020Accurate RGB-D Salient Object Detection via Collaborative Learningcited by 52
  18. 2020BaFFLe: Backdoor detection via Feedback-based Federated Learningcited by 52
  19. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
  20. 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
  21. 2018Deep feature compression for collaborative object detectioncited by 48
  22. 2022SPAct: Self-supervised Privacy Preservation for Action Recognitioncited by 47
  23. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  24. 2018An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videoscited by 45
  25. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  26. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  27. 2020Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligencecited by 39
  28. 2022Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingcited by 39
  29. 2020WAFFLE: Watermarking in Federated Learningcited by 34
  30. 2021Group Collaborative Learning for Co-Salient Object Detectioncited by 32
  31. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  32. 2019Real-World Image Datasets for Federated Learningcited by 30
  33. 2023Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalizationcited by 28
  34. 2021ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Trainingcited by 27
  35. 2023A Survey on Decentralized Federated Learningcited by 27
  36. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
  37. 2021Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learningcited by 25
  38. 2019Secure and Efficient Federated Transfer Learningcited by 20

50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 10; 2026: 3.

22 papers.

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  1. 2024Automatic Speech Recognition using Advanced Deep Learning Approaches: A surveycited by 50
  2. 2024Learning from the Web: Language Drives Weakly-Supervised Incremental Learning for Semantic Segmentationcited by 5
  3. 2024When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weathercited by 4
  4. 2024Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectivescited by 4
  5. 2025Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Modelscited by 4
  6. 2025Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detectioncited by 4
  7. 2024Deep Transfer Learning for Kidney Cancer Diagnosiscited by 3
  8. 2024A Bayesian Framework for Clustered Federated Learningcited by 3
  9. 2025Recent Advances in Transformer and Large Language Models for UAV Applicationscited by 3
  10. 2024Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Reviewcited by 2
  11. 2024Split Learning in Computer Vision for Semantic Segmentation Delay Minimizationcited by 2
  12. 2025FedPromo: Federated Lightweight Proxy Models at the Edge Bring New Domains to Foundation Modelscited by 2
10 more
  1. 2025Identity Card Presentation Attack Detection: A Systematic Reviewcited by 2
  2. 2024Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A surveycited by 1
  3. 2025Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attentioncited by 1
  4. 2025One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labelingcited by 0
  5. 2025Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosiscited by 0
  6. 2025Hierarchical Graph Neural Network for Compressed Speech Steganalysiscited by 0
  7. 2025Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systemscited by 0
  8. 2026Federated Medical Image Classification under Class and Domain Imbalance exploiting Synthetic Sample Generationcited by 0
  9. 2026Tiny Collaborative Inference for Occlusion-Robust Object Detectioncited by 0
  10. 2026EFFEKT: Efficient Federated Knowledge Transfer to Foundation Modelscited by 0