Federated Variational Inference for Bayesian Mixture Models
This addresses the need for privacy-preserving clustering in domains like healthcare, though it is incremental as it adapts existing variational inference techniques to a federated setting.
The paper tackles the problem of Bayesian model-based clustering for large-scale binary and categorical datasets by developing a federated learning approach using variational inference with local and global merge moves. The method performs competitively compared to existing clustering algorithms and is validated on electronic health record data.
We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled 'divide and conquer' inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by 'global' merge moves across batches to find global clustering structures. We show that these merge moves require only summaries of the data in each batch, enabling federated learning across local nodes without requiring the full dataset to be shared. Empirical results on simulated and benchmark datasets demonstrate that our method performs well in comparison to existing clustering algorithms. We validate the practical utility of the method by applying it to large scale electronic health record (EHR) data.