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[2602.19414] Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning
Summary
This paper presents a federated framework for causal representation learning in state-space systems, enabling decentralized counterfactual reasoning among interdependent industrial assets while preserving data privacy.
Why It Matters
As industries increasingly rely on interconnected systems, understanding how changes in one asset affect others is crucial. This research addresses the challenge of high-dimensional, private data by proposing a decentralized approach that maintains privacy while allowing for effective counterfactual reasoning.
Key Takeaways
- Introduces a federated framework for causal representation learning.
- Enables decentralized counterfactual reasoning without centralizing sensitive data.
- Demonstrates scalability and accuracy in cross-client inference through experiments.
Computer Science > Machine Learning arXiv:2602.19414 (cs) [Submitted on 23 Feb 2026] Title:Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning Authors:Nazal Mohamed, Ayush Mohanty, Nagi Gebraeel View a PDF of the paper titled Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning, by Nazal Mohamed and 2 other authors View PDF HTML (experimental) Abstract:Networks of interdependent industrial assets (clients) are tightly coupled through physical processes and control inputs, raising a key question: how would the output of one client change if another client were operated differently? This is difficult to answer because client-specific data are high-dimensional and private, making centralization of raw data infeasible. Each client also maintains proprietary local models that cannot be modified. We propose a federated framework for causal representation learning in state-space systems that captures interdependencies among clients under these constraints. Each client maps high-dimensional observations into low-dimensional latent states that disentangle intrinsic dynamics from control-driven influences. A central server estimates the global state-transition and control structure. This enables decentralized counterfactual reasoning where clients predict how outputs would change under alternative control inputs at others while only exchanging compact latent states. We prove ...
