Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning
This addresses scalability issues in clustering for distributed and federated learning settings, though it is incremental as it adapts existing methods to a new context.
The paper tackles the slow inference problem of Dirichlet Process Mixture Models (DPMMs) for clustering by proposing a distributed collapsed Gibbs sampler (DisCGS) for federated learning, achieving a 200x speedup (from 12 hours to 3 minutes for 100K data points) without performance loss.
Dirichlet Process Mixture Models (DPMMs) are widely used to address clustering problems. Their main advantage lies in their ability to automatically estimate the number of clusters during the inference process through the Bayesian non-parametric framework. However, the inference becomes considerably slow as the dataset size increases. This paper proposes a new distributed Markov Chain Monte Carlo (MCMC) inference method for DPMMs (DisCGS) using sufficient statistics. Our approach uses the collapsed Gibbs sampler and is specifically designed to work on distributed data across independent and heterogeneous machines, which habilitates its use in horizontal federated learning. Our method achieves highly promising results and notable scalability. For instance, with a dataset of 100K data points, the centralized algorithm requires approximately 12 hours to complete 100 iterations while our approach achieves the same number of iterations in just 3 minutes, reducing the execution time by a factor of 200 without compromising clustering performance. The code source is publicly available at https://github.com/redakhoufache/DisCGS.