QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. (arXiv:2106.00797v3 [cs.LG] UPDATED)

The objective of Federated Learning (FL) is to perform statistical inference
for data which are decentralised and stored locally on networked clients. FL
raises many constraints which include privacy and data ownership, communication
overhead, statistical heterogeneity, and partial client participation. In this
paper, we address these problems in the framework of the Bayesian paradigm. To
this end, we propose a novel federated Markov Chain Monte Carlo algorithm,
referred to as Quantised Langevin Stochastic Dynamics which may be seen as an …

arxiv bayesian dynamics federated learning learning stochastic