Communication-Efficient Federated Latent Class Analysis

Thomas M. Menino Convention & Exhibition Center 

Multi-institutional clinical registries provide valuable opportunities for disease phenotyping but are often constrained by data privacy requirements that preclude centralized analysis. We propose SOLO-fedLCA, a single-round federated latent class analysis framework that enables multi-center phenotyping without sharing individual-level data. SOLO-fedLCA uses a one-time broadcast of aggregated derivative information to construct an estimator that is asymptotically equivalent to pooled-data analysis, while substantially reducing communication overhead. We apply SOLO-fedLCA to a seven-center peripheral artery disease registry and identify three clinically interpretable phenotypes that differ in systemic atherosclerotic burden and limb ischemic severity. The resulting phenotypes stratify 12-month risk of major adverse limb events. Substantial between-center variation in phenotype prevalence underscores the need for federated methods to support reproducible multi-institutional phenotyping. Overall, SOLO-fedLCA provides a practical and statistically efficient framework for collaborative clinical research under real-world data-sharing constraints.

Federated inference

Latent class analysis

One-shot estimation

Majorization–minimization 

Biometrics Section