  Federated Bayesian learning offers a principled framework for the definition
of collaborative training algorithms that are able to quantify epistemic
uncertainty and to produce trustworthy decisions. Upon the completion of
collaborative training, an agent may decide to exercise her legal "right to be
forgotten", which calls for her contribution to the jointly trained model to be
deleted and discarded. This paper studies federated learning and unlearning in
a decentralized network within a Bayesian framework. It specifically develops
federated variational inference (VI) solutions based on the decentralized
solution of local free energy minimization problems within exponential-family
models and on local gossip-driven communication. The proposed protocols are
demonstrated to yield efficient unlearning mechanisms.
