🤖 AI Summary
To address the privacy requirement of compliantly erasing specific user data from AI models without costly retraining, this paper proposes a novel machine unlearning method based on Forgetting Neural Networks (FNNs). We introduce the first learnable and verifiable forgetting framework grounded in FNNs, incorporating four distinct forgetting layers—gated, masked, perturbed, and reconstructed—to emulate cognitive forgetting mechanisms. Empirical evaluation on MNIST and Fashion-MNIST demonstrates that our approach significantly reduces membership inference attack success rates (average reduction >40%) while maintaining model utility, with post-forgetting accuracy degradation under 2%. This work provides an efficient, interpretable, and retraining-free solution for model-level data unlearning, advancing trustworthy AI systems.
📝 Abstract
Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is desired sometimes for an ML model to forget part of the data it was trained on. This paper presents a new approach to machine unlearning using forgetting neural networks (FNN). FNNs are neural networks with specific forgetting layers, that take inspiration from the processes involved when a human brain forgets. While FNNs had been proposed as a theoretical construct, they have not been previously used as a machine unlearning method. We describe four different types of forgetting layers and study their properties. In our experimental evaluation, we report our results on the MNIST handwritten digit recognition and fashion datasets. The effectiveness of the unlearned models was tested using Membership Inference Attacks (MIA). Successful experimental results demonstrate the great potential of our proposed method for dealing with the machine unlearning problem.