AI RESEARCH
Towards Uncertainty-Aware Federated Granger Causal Learning
arXiv CS.LG
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ArXi:2602.13004v2 Announce Type: replace Granger causality recovers directed interactions from time-series data, but in many distributed systems, the data are vertically partitioned across clients, with each client observing only the variables of its own subsystem. Federated Granger causality (FedGC) recovers cross-client interactions without sharing raw data. Existing FedGC methods, however, return deterministic point estimates with no calibrated measure of uncertainty, leaving operators without a principled basis for identifying reliable cross-client interactions.