Decoupling Neural Networks to Leverage Uniform Representation and Balance Personalization and Collaboration in...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Federated learning (FL) enables collaborative model training while preserving data privacy.
• Data heterogeneity across clients in FL negatively impacts global model performance and generalization.
• Existing FL methods struggle to effectively address non-IID (non-independently and identically distributed) data distributions.

Purpose of the Study:

• To propose FedUB, a novel personalized federated learning framework to mitigate data heterogeneity.
• To introduce a uniform feature representation (UR) and a classifier balancing personalization and collaboration.
• To theoretically and empirically validate the effectiveness of FedUB in improving FL performance.

Main Methods:

• FedUB employs a shared feature extractor and a common representation centroid (RC) for uniform feature representation.
• A regularization term is incorporated to minimize the discrepancy between global and local RCs, enforcing uniformity.
• Classifier parameters are partitioned into personalized and collaborated components based on importance estimation to balance local adaptation and global knowledge.

Main Results:

• Theoretical analysis confirms the existence of UR and its ability to reduce the average generalization bound.
• Experiments on benchmark datasets show significant performance gains compared to existing FL approaches.
• FedUB demonstrates improved generalization behavior, effectively handling data heterogeneity.

Conclusions:

• FedUB successfully addresses the challenge of data heterogeneity in federated learning through uniform feature representation and a hybrid classifier.
• The proposed framework enhances model performance and generalization by balancing client-specific adaptation with collaborative learning.
• FedUB offers a promising solution for building robust and privacy-preserving federated learning systems in diverse data environments.