We propose a new algorithm for deep generative modeling of sequence data in continuous spaces based on a novel adaptation of operator theory for probabilistic dynamical systems.

This paper demonstrates that applying adaptive latent-space constraints in personalized FL algorithms improves performance across a number of challenging benchmark tasks, especially those with significant feature heterogeneity