FedeCouple: Fine-Grained Balancing of Global-Generalization and Local-Adaptability in Federated Learning

• URL: http://arxiv.org/abs/2511.09599v1
• Date: Fri, 14 Nov 2025 01:01:13 GMT
• Title: FedeCouple: Fine-Grained Balancing of Global-Generalization and Local-Adaptability in Federated Learning
• Authors: Ming Yang, Dongrun Li, Xin Wang, Feng Li, Lisheng Fan, Chunxiao Wang, Xiaoming Wu, Peng Cheng,
• Abstract summary: In privacy-preserving mobile network transmission scenarios, personalized learning methods have demonstrated notable advantages in learning.<n>We propose Fede, a federated learning that balances global and local feature representations at a fine-grained level.<n>In experiments evaluating effectiveness, Fede surpasses the best baseline by a significant margin of 4.3%.
• Score: 22.362030807503714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In privacy-preserving mobile network transmission scenarios with heterogeneous client data, personalized federated learning methods that decouple feature extractors and classifiers have demonstrated notable advantages in enhancing learning capability. However, many existing approaches primarily focus on feature space consistency and classification personalization during local training, often neglecting the local adaptability of the extractor and the global generalization of the classifier. This oversight results in insufficient coordination and weak coupling between the components, ultimately degrading the overall model performance. To address this challenge, we propose FedeCouple, a federated learning method that balances global generalization and local adaptability at a fine-grained level. Our approach jointly learns global and local feature representations while employing dynamic knowledge distillation to enhance the generalization of personalized classifiers. We further introduce anchors to refine the feature space; their strict locality and non-transmission inherently preserve privacy and reduce communication overhead. Furthermore, we provide a theoretical analysis proving that FedeCouple converges for nonconvex objectives, with iterates approaching a stationary point as the number of communication rounds increases. Extensive experiments conducted on five image-classification datasets demonstrate that FedeCouple consistently outperforms nine baseline methods in effectiveness, stability, scalability, and security. Notably, in experiments evaluating effectiveness, FedeCouple surpasses the best baseline by a significant margin of 4.3%.

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