Federated Joint Learning for Domain and Class Generalization

• URL: http://arxiv.org/abs/2601.12253v2
• Date: Tue, 27 Jan 2026 07:46:51 GMT
• Title: Federated Joint Learning for Domain and Class Generalization
• Authors: Haoran Xu, Jiaze Li, Jianzhong Ju, Zhenbo Luo,
• Abstract summary: textbfFedDCG is a novel approach that addresses both class and domain generalization in federated learning settings.<n>Experiments show that textbfFedDCG outperforms state-of-the-art baselines in terms of accuracy and robustness.
• Score: 15.177261433209301
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Efficient fine-tuning of visual-language models like CLIP has become crucial due to their large-scale parameter size and extensive pretraining requirements. Existing methods typically address either the issue of unseen classes or unseen domains in isolation, without considering a joint framework for both. In this paper, we propose \textbf{Fed}erated Joint Learning for \textbf{D}omain and \textbf{C}lass \textbf{G}eneralization, termed \textbf{FedDCG}, a novel approach that addresses both class and domain generalization in federated learning settings. Our method introduces a domain grouping strategy where class-generalized networks are trained within each group to prevent decision boundary confusion. During inference, we aggregate class-generalized results based on domain similarity, effectively integrating knowledge from both class and domain generalization. Specifically, a learnable network is employed to enhance class generalization capabilities, and a decoupling mechanism separates general and domain-specific knowledge, improving generalization to unseen domains. Extensive experiments across various datasets show that \textbf{FedDCG} outperforms state-of-the-art baselines in terms of accuracy and robustness.

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