解决联邦学习中数据不均与通信开销大的问题
GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task
- 用高斯混合模型生成类别原型，融合跨客户端知识
- 在数据不平衡下提升准确率3.6%，通信开销低
- 适合医疗影像等资源受限场景的联邦学习
联邦学习（FL）支持去中心化图像的安全利用，在医学图像识别和自动驾驶中具有应用前景。但实际部署面临两大挑战：模型更新偏向多数类特征，导致知识融合无效；频繁传输高维参数造成巨大通信开销。受人类大脑高效整合知识的启发，我们提出生成式联邦原型学习（GFPL）框架。该框架采用基于高斯混合模型（GMM）的原型生成方法，捕捉类别特征的统计信息，并利用巴氏距离实现语义相似知识的聚合。进一步地，通过融合后的原型生成伪特征，缓解客户端间特征分布不均的问题。为增强本地训练中的特征对齐，设计了结合点积回归与交叉熵的双分类器架构。大量实验表明，在数据不平衡设置下，GFPL提升模型准确率3.6%的同时保持低通信成本。
原文摘要 · Abstract (English)
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.
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