Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning

ArXi:2602.19414v2 Announce Type: replace 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