Connected vehicles train a driving model through federated learning (FL) and drive with it, so a faulty contribution often surfaces only as a delayed symptom on the road.
Federated unlearning (FU) assumes that a removal request names what to forget. A symptom names no vehicle or round, and aggregation has already spread its cause across the fleet.
We propose A-ggregation-T-operational-evidence Neutralization of Effects (\method{})
\method{} projects everyday driving errors and reported symptoms onto a subspace spanned by recent global increments.
It propagates the surviving effect of every contribution with curvature estimated from these increments and scores all contributions in one backward pass.
A two-tier correction suppresses suspected effects in every round and eliminates confirmed ones after a one-round probe.
We prove that backward scoring matches forward scoring and that the elimination is exact when training is flat outside the subspace.
Experiments
On three driving datasets, \method{} leaves 43% to 61% less residual harm than the strongest baseline in eight of nine settings while staying within 0.36 points of full retraining in utility.