  Machine Unlearning (MU) is an increasingly important topic in machine
learning safety, aiming at removing 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. While
several FU methods have been proposed, we currently lack a general approach
providing formal unlearning guarantees to the FedAvg routine, while ensuring
scalability and generalization beyond the convex assumption on the clients'
loss functions. We aim at filling this gap by proposing SIFU (Sequential
Informed Federated Unlearning), a new FU method applying to both convex and
non-convex optimization regimes. SIFU naturally applies to FedAvg without
additional computational cost for the clients and provides formal guarantees on
the quality of the unlearning task. We provide a theoretical analysis of the
unlearning properties of SIFU, and practically demonstrate its effectiveness as
compared to a panel of unlearning methods from the state-of-the-art.
