On Convergence of Federated Averaging Langevin Dynamics

Wei Deng, Qian Zhang, Yian Ma, Zhao Song, Guang Lin

Keywords: Langevin dynamics, federated learning, posterior inference, MCMC, stochastic gradient Langevin dynamics, differential privacy

TL;DR: A federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions in federated learning.

Abstract: We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients. In particular, we generalize beyond normal posterior distributions and consider a general class of models. We develop theoretical guarantees for FA-LD for strongly log-concave distributions with non-i.i.d data and study how the injected noise and the stochastic-gradient noise, the heterogeneity of data, and the varying learning rates affect the convergence. Such an analysis sheds light on the optimal choice of local updates to minimize communication cost. Important to our approach is that the communication efficiency does not deteriorate with the injected noise in the Langevin algorithms. In addition, we examine in our FA-LD algorithm both independent and correlated noise used over different clients. We observe there is a trade-off between the pairs among communication, accuracy, and data privacy. As local devices may become inactive in federated networks, we also show convergence results based on different averaging schemes where only partial device updates are available. In such a case, we discover an additional bias that does not decay to zero.

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