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

2026-02-25

AI Summary

This work proposes a generative federated prototype learning framework to address two key challenges in federated learning: model bias toward majority classes caused by data imbalance and high communication overhead due to the transmission of high-dimensional parameters. The approach constructs class-level feature prototypes using Gaussian mixture models and aggregates semantically similar knowledge across clients via Bhattacharyya distance. To mitigate inter-client distributional heterogeneity, it generates synthetic features that augment local representations. A dual-classifier architecture combined with a hybrid loss function—integrating Dot regression and cross-entropy—is employed to optimize local training. Experimental results demonstrate that the proposed method improves accuracy by 3.6% under imbalanced settings while significantly reducing communication costs.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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 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.

Problem

• Federated Learning
• Data Imbalance
• Communication Overhead
• Resource-Constrained
• Knowledge Fusion

Innovation

• Generative Federated Prototype Learning
• Gaussian Mixture Model
• Bhattacharyya distance
• feature imbalance
• communication efficiency