FedDis: A Causal Disentanglement Framework for Federated Traffic Prediction

• URL: http://arxiv.org/abs/2601.22578v1
• Date: Fri, 30 Jan 2026 05:20:53 GMT
• Title: FedDis: A Causal Disentanglement Framework for Federated Traffic Prediction
• Authors: Chengyang Zhou, Zijian Zhang, Chunxu Zhang, Hao Miao, Yulin Zhang, Kedi Lyu, Juncheng Hu,
• Abstract summary: We introduce FedDis, a novel framework for federated spatial-temporal prediction.<n>We show that FedDis consistently achieves state-of-the-art performance datasets, promising efficiency, and superior expandability.
• Score: 14.618528630951579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning offers a promising paradigm for privacy-preserving traffic prediction, yet its performance is often challenged by the non-identically and independently distributed (non-IID) nature of decentralized traffic data. Existing federated methods frequently struggle with this data heterogeneity, typically entangling globally shared patterns with client-specific local dynamics within a single representation. In this work, we postulate that this heterogeneity stems from the entanglement of two distinct generative sources: client-specific localized dynamics and cross-client global spatial-temporal patterns. Motivated by this perspective, we introduce FedDis, a novel framework that, to the best of our knowledge, is the first to leverage causal disentanglement for federated spatial-temporal prediction. Architecturally, FedDis comprises a dual-branch design wherein a Personalized Bank learns to capture client-specific factors, while a Global Pattern Bank distills common knowledge. This separation enables robust cross-client knowledge transfer while preserving high adaptability to unique local environments. Crucially, a mutual information minimization objective is employed to enforce informational orthogonality between the two branches, thereby ensuring effective disentanglement. Comprehensive experiments conducted on four real-world benchmark datasets demonstrate that FedDis consistently achieves state-of-the-art performance, promising efficiency, and superior expandability.

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