Vector Quantization-Based Clustered Federated Learning With Global Feature Anchors for Improved Representation and...

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Federated Learning (FL) enables collaborative model training without sharing raw data.
• Data heterogeneity across clients poses a significant challenge in FL.
• Clustered Federated Learning (CFL) aims to address heterogeneity by grouping clients, but existing methods struggle with accurate data representation.

Purpose of the Study:

• To propose a novel Clustered Federated Learning (CFL) framework, Vector Quantization-based CFL (VQCFL), to overcome limitations in representing client data heterogeneity.
• To enhance the accuracy of client clustering and improve model personalization and generalization in FL.

Main Methods:

• Introduced a Vector Quantization Network (VQNet) to map local client feature spaces to discrete feature dictionary vectors, capturing intrinsic data structures.
• Implemented a global feature anchor strategy to prevent feature dictionary vector drift and ensure consistent cross-client updates.
• Developed a cross-cluster knowledge-sharing mechanism using aggregated feature dictionary vectors and a personalized classifier weight adjustment strategy.

Main Results:

• VQCFL effectively captures intrinsic client data features through vector quantization.
• The global feature anchor strategy ensures stable and consistent feature representations across clients.
• The cross-cluster knowledge-sharing mechanism significantly improves generalization performance, especially with mixed data heterogeneity.

Conclusions:

• VQCFL offers a superior approach to handling data heterogeneity in federated learning compared to existing CFL methods.
• The framework achieves enhanced local personalization and robust global generalization performance.
• VQCFL provides a promising direction for more effective and accurate clustered federated learning systems.