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GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task

arXiv 2602.21873 ↗First posted 25 Feb 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) facilitates the secure utilization of decentralized images, advancing applications in medical image recognition and autonomous driving. However, conventional FL faces two critical challenges in real-world deployment: ineffective knowledge fusion caused by model updates biased toward majority-class features, and prohibitive communication overhead due to frequent transmissions of high-dimensional model parameters. Inspired by the human brain's efficiency in knowledge integration, we propose a novel Generative Federated Prototype Learning (GFPL) framework to address these issues. Within this framework, a prototype generation method based on Gaussian Mixture Model (GMM) captures the statistical information of class-wise features, while a prototype aggregation strategy using Bhattacharyya distance effectively fuses semantically similar knowledge across clients. In addition, these fused prototypes are leveraged to generate pseudo-features, thereby mitigating feature distribution imbalance across clients. To further enhance feature alignment during local training, we devise a dual-classifier architecture, optimized via a hybrid loss combining Dot Regression and Cross-Entropy. Extensive experiments on benchmarks show that GFPL improves model accuracy by 3.6% under imbalanced data settings while maintaining low communication cost.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 56 references that are in the map. First posted on arXiv 25 Feb 2026.

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References · 40

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  7. 2018LEAF: A Benchmark for Federated Settingscited by 780
  8. 2021Model-Contrastive Federated Learningcited by 762
  9. 2020Prevalence of Neural Collapse during the terminal phase of deep learning trainingcited by 713
  10. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  11. 2019Federated Learning with Personalization Layerscited by 533
  12. 2021Image Segmentation Using Text and Image Promptscited by 509
28 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  4. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  5. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  6. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  7. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  8. 2022Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?cited by 100
  9. 2022Tackling Data Heterogeneity in Federated Learning with Class Prototypescited by 66
  10. 2022FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learningcited by 65
  11. 2022Cross-modal Prototype Driven Network for Radiology Report Generationcited by 58
  12. 2023No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifiercited by 55
  13. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 49
  14. 2024FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learningcited by 47
  15. 2022Generating Representative Samples for Few-Shot Classificationcited by 39
  16. 2022On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyondcited by 37
  17. 2023FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clientscited by 23
  18. 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
  19. 2020Learning Sparse Prototypes for Text Generationcited by 15
  20. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14
  21. 2023Feature Matching Data Synthesis for Non-IID Federated Learningcited by 13
  22. 2022Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Datacited by 12
  23. 2023A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extractioncited by 12
  24. 2023A Bag-of-Prototypes Representation for Dataset-Level Applicationscited by 11
  25. 2024A Survey on Federated Learning in Intelligent Transportation Systemscited by 9
  26. 2024SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overheadcited by 9
  27. 2024Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Drivingcited by 8
  28. 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysiscited by 8

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