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[2602.21873] GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task
Summary
The GFPL framework enhances federated learning by addressing data imbalance and communication overhead in resource-constrained vision tasks, improving model accuracy significantly.
Why It Matters
This research is crucial as it tackles common challenges in federated learning, particularly in fields like medical imaging and autonomous driving, where data privacy and efficiency are paramount. By improving model accuracy while reducing communication costs, GFPL has the potential to advance practical applications in these areas.
Key Takeaways
- GFPL addresses knowledge fusion issues in federated learning.
- Utilizes Gaussian Mixture Model for effective prototype generation.
- Implements a dual-classifier architecture to enhance feature alignment.
- Improves model accuracy by 3.6% in imbalanced data settings.
- Reduces communication overhead, making it suitable for resource-constrained environments.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21873 (cs) [Submitted on 25 Feb 2026] Title:GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task Authors:Shiwei Lu, Yuhang He, Jiashuo Li, Qiang Wang, Yihong Gong View a PDF of the paper titled GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task, by Shiwei Lu and 4 other authors View PDF HTML (experimental) Abstract: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 distribut...