Exploring Nonlinear Pathway in Parameter Space for Machine Unlearning

TL;DR

This paper introduces Mode Connectivity Unlearning (MCU), a nonlinear pathway-based framework for machine unlearning that improves effectiveness, efficiency, and flexibility over traditional linear methods, validated through extensive image classification experiments.

Contribution

The paper proposes a novel nonlinear unlearning framework using mode connectivity, along with a parameter mask and adaptive penalty strategies, enhancing unlearning performance and flexibility.

Findings

MCU outperforms existing methods in unlearning efficacy.

The framework uncovers a spectrum of unlearning models along the pathway.

Experimental results demonstrate superior performance on image classification tasks.

Abstract

Machine Unlearning (MU) aims to remove the information of specific training data from a trained model, ensuring compliance with privacy regulations and user requests. While one line of existing MU methods relies on linear parameter updates via task arithmetic, they suffer from weight entanglement. In this work, we propose a novel MU framework called Mode Connectivity Unlearning (MCU) that leverages mode connectivity to find an unlearning pathway in a nonlinear manner. To further enhance performance and efficiency, we introduce a parameter mask strategy that not only improves unlearning effectiveness but also reduces computational overhead. Moreover, we propose an adaptive adjustment strategy for our unlearning penalty coefficient to adaptively balance forgetting quality and predictive performance during training, eliminating the need for empirical hyperparameter tuning. Unlike traditional MU methods that identify only a single unlearning model, MCU uncovers a spectrum of unlearning models along the pathway. Overall, MCU serves as a plug-and-play framework that seamlessly integrates with any existing MU methods, consistently improving unlearning efficacy. Extensive experiments on the image classification task demonstrate that MCU achieves superior performance. The codes are available at https://github.com/TIML-Group/Mode-Connectivity-Unlearning.