Federated Expectation Maximization with heterogeneity mitigation and variance reduction

Understand

The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models.

• As in any other field of machine learning, applications of latent variable models to very large datasets make the use of advanced parallel and distributed architectures mandatory.
• This paper introduces FedEM, which is the first extension of the EM algorithm to the federated learning context.
• FedEM is a new communication efficient method, which handles partial participation of local devices, and is robust to heterogeneous distributions of the datasets.