Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
<|reference_start|>Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference: Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introduces the first comprehensive framework for fitting Bayesian models in the VFL setting. We propose a novel approach that leverages data augmentation techniques to transform VFL problems into a form compatible with existing Bayesian federated learning algorithms. We present an innovative model formulation for specific VFL scenarios where the joint likelihood factorizes into a product of client-specific likelihoods. To mitigate the dimensionality challenge posed by data augmentation, which scales with the number of observations and clients, we develop a factorized amortized variational approximation that achieves scalability independent of the number of observations. We showcase the efficacy of our framework through extensive numerical experiments on logistic regression, multilevel regression, and a novel hierarchical Bayesian split neural net model. Our work paves the way for privacy-preserving, decentralized Bayesian inference in vertically partitioned data scenarios, opening up new avenues for research and applications in various domains.<|reference_end|>
@article{hassan2024scalable,
    title={Scalable Vertical Federated Learning via Data Augmentation and Amortized
  Inference},
    author={Conor Hassan, Matthew Sutton, Antonietta Mira, Kerrie Mengersen},
    journal={arXiv preprint arXiv:2405.04043},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04043},
    primaryClass={stat.CO cs.LG stat.ME stat.ML}
}