[Submitted on 15 Oct 2026]
Abstract: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 underline{A}ggregation-underline{T}ime underline{O}perational-evidence underline{N}eutralization of underline{E}ffects (method{}), which makes unlearning a term of the FL aggregation rule. 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. 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.
Submission history
From: Jun Huang [view email]
[v1] Thu, 15 Oct 2026 19:34:46 UTC (2,789 KB)
