The aim of Machine Unlearning (MU) is to provide theoretical guarantees on
the removal of the contribution of a given data point from a training
procedure. Federated Unlearning (FU) consists in extending MU to unlearn a
given client's contribution from a federated training routine. Current FU
approaches are generally not scalable, and do not come with sound theoretical
quantification of the effectiveness of unlearning. In this work we present
Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU
approach. Upon unlearning request from a given client, IFU identifies the
optimal FL iteration from which FL has to be reinitialized, with unlearning
guarantees obtained through a randomized perturbation mechanism. The theory of
IFU is also extended to account for sequential unlearning requests.
Experimental results on different tasks and dataset show that IFU leads to more
efficient unlearning procedures as compared to basic re-training and
state-of-the-art FU approaches