Scaling Probabilistic Circuits via Data Partitioning
This work addresses scalability issues in probabilistic modeling for researchers and practitioners dealing with large, distributed datasets, though it is incremental as it builds on existing PC and federated learning concepts.
The paper tackles the challenge of scaling probabilistic circuits (PCs) to larger datasets by introducing federated circuits (FCs), a framework that enables distributed learning across multiple machines through data partitioning, resulting in faster training and unified handling of horizontal, vertical, and hybrid federated learning scenarios.
Probabilistic circuits (PCs) enable us to learn joint distributions over a set of random variables and to perform various probabilistic queries in a tractable fashion. Though the tractability property allows PCs to scale beyond non-tractable models such as Bayesian Networks, scaling training and inference of PCs to larger, real-world datasets remains challenging. To remedy the situation, we show how PCs can be learned across multiple machines by recursively partitioning a distributed dataset, thereby unveiling a deep connection between PCs and federated learning (FL). This leads to federated circuits (FCs) -- a novel and flexible federated learning (FL) framework that (1) allows one to scale PCs on distributed learning environments (2) train PCs faster and (3) unifies for the first time horizontal, vertical, and hybrid FL in one framework by re-framing FL as a density estimation problem over distributed datasets. We demonstrate FC's capability to scale PCs on various large-scale datasets. Also, we show FC's versatility in handling horizontal, vertical, and hybrid FL within a unified framework on multiple classification tasks.