Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting

• URL: http://arxiv.org/abs/2607.24218v1
• Date: Mon, 27 Jul 2026 09:50:08 GMT
• Title: Every Client Is an Environment: Federated De-confounding for Spatio-Temporal Forecasting
• Abstract summary: We propose method, a novel federated de-confounding framework that textbfs clients as distinct causal.<n>We derive a theoretical federated de-confounding bound that is linearly controlled by the averaged confounding strength.<n>Experiments demonstrate method consistently outperforms federated baselines, while providing transferable, interpretable, and communication-efficient environmental representations.
• Score: 39.11762381290371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning has emerged as a promising paradigm for spatio-temporal forecasting (STF), enabling collaborative model training without sharing raw observations. Existing federated STF methods primarily regard cross-client heterogeneity as an optimization challenge and mitigate it through personalized approaches. However, such heterogeneity fundamentally stems from diverse \emph{environmental conditions}, and these methods capture environment-specific forecasting patterns, hardly generalizing under environmental shifts. Our key insight is that the environmental diversity across federated clients should be exploited, as they provide \emph{complementary observations of the same underlying spatio-temporal system}. Based on this insight, we propose \method, a novel federated de-confounding framework that \textbf{treats clients as distinct causal environments}. \method leverages the client heterogeneity as distributed environmental evidence and learns a global prototype codebook to capture shared environmental regimes. We further derive a theoretical federated de-confounding bound that is linearly controlled by the averaged confounding strength. Extensive experiments demonstrate that \method consistently outperforms federated baselines, while providing transferable, interpretable, and communication-efficient environmental representations.

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