Federated Nonlinear Causal Discovery via Divide-and-Conquer Learning
TL;DR
DC-FNCD (Divide-and-Conquer based Federated Nonlinear Causal Discovery) is proposed, which decomposes the global problem into independent per-variable neighborhood learning tasks, enabling lossless federated aggregation without raw data exchange.
Abstract
Federated causal discovery aims to learn causal structures from distributed data without sharing raw samples. Existing federated nonlinear methods adopt a monolithic global strategy that optimizes the entire graph simultaneously, suffering from catastrophic error propagation: a single misidentified edge cascades through the global structure, severely degrading accuracy under heterogeneous and limited local data. We propose DC-FNCD (Divide-and-Conquer based Federated Nonlinear Causal Discovery), which decomposes the global problem into independent per-variable neighborhood learning tasks. The core mechanism is a characteristic function-based conditional independence test whose empirical statistics admit exact linear decomposition across clients, enabling lossless federated aggregation without raw data exchange. Local neighborhoods are merged via conflict-aware skeleton construction and oriented through federated additive noise model testing. Extensive experiments demonstrate that DC-FNCD significantly outperforms state-of-the-art federated baselines. The source code is available at https://github.com/Xianjie-Guo/DC-FNCD.