Federated Variational Inference Methods for Structured Latent Variable Models

Conor Hassan; Robert Salomone; Kerrie Mengersen

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arXiv · 2302.03314

Federated Variational Inference Methods for Structured Latent Variable Models

Conor Hassan·Robert Salomone·Kerrie Mengersen

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Abstract

Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limited, excluding many structured probabilistic models. We present a general and elegant solution based on structured variational inference, widely used in Bayesian machine learning, adapted for the federated setting. Additionally, we provide a communication-efficient variant analogous to the canonical FedAvg algorithm. The proposed algorithms' effectiveness is demonstrated, and their performance is compared with hierarchical Bayesian neural networks and topic models.

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Conor Hassan, Robert Salomone, Kerrie Mengersen. 2023-02-07. Federated Variational Inference Methods for Structured Latent Variable Models. https://arxiv.org/abs/2302.03314

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