CoFlo: Federated Sampling of Molecular Conformers

Generative models for molecular conformer generation benefit from large, diverse training datasets. However, much of the available molecular data remains siloed across institutions with different chemical specializations, where privacy constraints and intellectual property concerns hinder centralized training. To address this challenge, we introduce CoFlo, a framework for collaborative conformer generation across distributed clients without sharing their training data or generative-model parameters. Clients independently train flow matching models on their local data, while a lightweight router is trained through federated learning. At inference time, the router combines the fixed client velocity fields to guide a shared sampling trajectory, using mixture weights motivated by a posterior-weighted decomposition of the joint velocity field. We evaluate CoFlo on GEOM-QM9 and GEOM-Drugs under IID and heterogeneous scaffold-, chemotype-, topology-based, and cross-dataset partitions. CoFlo outperforms average client performance across all four metrics in every evaluated setting and surpasses even the best individual client on all four metrics in most dataset–split combinations, including the realistic mixed-dataset setting. These results demonstrate the potential of inference-time collaboration of specialized experts for conformer generation.