FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning

As an emerging research direction, federated causal structure learning (CSL) aims at learning causal relationships from decentralized data across multiple clients while preserving data privacy. Existing federated CSL algorithms suffer from scalability and accuracy issues, since they require computationally expensive CSL algorithms to be executed at each client. Furthermore, in real-world scenarios, the number of samples held by each client varies significantly, and existing methods still assign equal weights to the learned structural information from each client, which severely harms the learning accuracy of those methods. To address these two limitations, we propose FedCSL, a scalable and accurate method for federated CSL. Specifically, FedCSL consists of two novel strategies: (1) a federated local-to-global learning strategy that enables FedCSL to scale to high-dimensional data for tackling the scalability issue, and (2) a novel weighted aggregation strategy that does not rely on any complex encryption techniques while preserving data privacy for tackling the accuracy issue. Extensive experiments on benchmark datasets, high-dimensional synthetic datasets and a real-world dataset verify the efficacy of the proposed FedCSL method. The source code is available at https://github.com/Xianjie-Guo/FedCSL.

Authors

• Xianjie Guo Hefei University of Technology, China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, China
• Kui Yu Hefei University of Technology, China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, China
• Lin Liu
• Jiuyong Li University of South Australia, Australia

Keywords

• DMKM: Scalability, Parallel & Distributed Systems
• ML: Causal Learning
• ML: Dimensionality Reduction/Feature Selection
• ML: Distributed Machine Learning & Federated Learning