Linear mixed-effects models play a fundamental role in statistical methodology. A variety of Markov chain Monte Carlo (MCMC) algorithms exist for fitting these models, but they are inefficient massive data settings because every iteration any such MCMC algorithm passes through the full data. Many divide-and-conquer methods have been proposed to solve this problem, lack theoretical guarantees, i...