FF-Erase: Machine Unlearning and Verification for Forward-Forward Models

The Forward-Forward (FF) algorithms provide a promising alternative to backpropagation (BP) by training neural networks through independent layer-wise goodness objectives. However, machine unlearning for FF models remains largely unexplored, which is a fundamental model function aiming to remove the influence of designated training samples from a trained model while preserving its utility on the remaining data. Directly adapting BP-oriented unlearning methods either causes ineffective unlearning or induces model collapse. This is because of two fundamental challenges specific to FF unlearning: (\romannumeral 1) each FF layer lacks a compatible unlearning target with the other layers (i.e., coordinated yet layer-specific directions), and (\romannumeral 2) the under/over-forgetting balance is independent across layers and thereby difficult to determine. To address these challenges, we propose FF-Erase, the first FF-specific machine unlearning framework. FF-Erase leverages a low-cost guidance model to instantiate a stable and structurally realizable goodness space. This establishes coordinated yet layer-specific anchors for precise goodness realignment, thereby avoiding the optimization collapse of independent layers. Such a goodness anchor also enables layer-independent unlearning adaptation, allowing FF-Erase to adjust the under/over-forgetting balance layer-wise. We further propose goodness-based membership inference (G-MIA), an FF-specific unlearning auditing tool that efficiently and accurately verifies the removal effectiveness for an FF unlearning algorithm. Extensive experiments across architectures, datasets, forgetting ratios, and unlearning scenarios show that FF-Erase consistently forgets target data effectively while preserving retained-data utility and avoiding model collapse. FF-Erase achieves up to $11.2\times$ speedup over retraining, establishing an effective and efficient foundation for machine unlearning on FF models.