Towards Privacy-Aware Causal Structure Learning in Federated Setting

Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it is impossible to centralize data from all sources and put them together as a single dataset. To preserve data privacy, federated learning as a new learning paradigm has attracted much attention in machine learning in recent years. In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data. Specifically, we first propose a novel layer-wise aggregation strategy for a seamless adaptation of the PC algorithm into the federated learning paradigm for federated skeleton learning, then we design an effective strategy for learning consistent separation sets for federated edge orientation. The extensive experiments validate that FedPC is effective for causal structure learning in a federated learning setting.Comment: This paper has been accepted by the journal IEEE Transactions on Big Data, and it contains 21 pages, 9 figures and 15 table

Spatiotemporal Evolution of Land Cover and Landscape Ecological Risk in Wuyishan National Park and Surrounding Areas

Previous research on national park conservation has predominantly concentrated on their internal regions, with scant attention given to the parks and their adjacent areas as integrated entities. Therefore, the investigation of land cover and landscape ecological risks in national parks and surrounding areas is essential for overall ecosystem protection and regional sustainable development. This study examines the spatiotemporal evolution of land cover and its landscape ecological risk in Wuyishan National Park, China, and its surrounding areas (WNPSA) from 1990 to 2020. The results show that (1) the land cover of WNPSA from 1990 to 2020 predominantly exhibited a consistent decline in forested areas, paralleled by an augmentation in farmland and impervious surface areas. The center of standard deviation ellipse of impervious surfaces has been progressively moving further south in tandem with the expansion of these surfaces, primarily located within the county town of Fujian Province. (2) The Wuyishan National Park (WNP) areas were dominated by low values of landscape index, and the high value areas in the park were mainly located at the provincial boundary area, with a gradual narrowing during 1990–2020, suggesting a decrease in landscape heterogeneity within the park. High value areas in the surrounding areas mainly occurred in areas with clustered impervious surfaces (e.g., the county town), where part of them located in Wuyishan City have spread to the edge area southeast of WNP. (3) From 1990 to 2020, the lowest-risk areas continue to expand. However, as the medium-risk zone of the adjacent region extends into the edge of the national park, the low risk zone within the national park exhibits a trend from continuous to separate with the neighboring low risk zone. This led to an escalating stress effect on the ecological security of both the adjacent regions and the national park’s boundary areas due to land cover changes

Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets

We consider the problem of reducing the false discovery rate in multiple high-dimensional interventional datasets under unknown targets. Traditional algorithms merged directly multiple causal graphs learned, which ignores the contradictions of different datasets, leading to lots of inconsistent directions of edges. For reducing the contradictory information, we propose a new algorithm, which first learns an interventional Markov equivalence class (I-MEC) before merging multiple graphs. It utilizes the full power of the constraints available in interventional data and combines ideas from local learning, intervention, and search-and-score techniques in a principled and effective way in different intervention experiments. Specifically, local learning on multiple datasets is used to build a causal skeleton. Perfect intervention destroys some possible triangles, leading to the identification of more possible V-structures. And then a theoretically correct I-MEC is learned. Search and scoring techniques based on the learned I-MEC further identify the remaining unoriented edges. Both theoretical analysis and experiments on benchmark Bayesian networks with the number of variables from 20 to 724 validate that the effectiveness of our algorithm in reducing the false discovery rate in high-dimensional interventional data