Fishers Harvest Parallel Unlearning in Inherited Model Networks

• URL: http://arxiv.org/abs/2408.08493v2
• Date: Tue, 20 Aug 2024 08:41:58 GMT
• Title: Fishers Harvest Parallel Unlearning in Inherited Model Networks
• Authors: Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu,
• Abstract summary: This paper presents a novel unlearning framework, which enables fully parallel unlearning among models exhibiting inheritance. A key enabler is the new Unified Model Inheritance Graph (UMIG), which captures the inheritance using a Directed Acyclic Graph (DAG) Our framework accelerates unlearning by 99% compared to alternative methods.
• Score: 26.47424619448623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlearning in various learning frameworks remains challenging, with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework, which enables fully parallel unlearning among models exhibiting inheritance. A key enabler is the new Unified Model Inheritance Graph (UMIG), which captures the inheritance using a Directed Acyclic Graph (DAG).Central to our framework is the new Fisher Inheritance Unlearning (FIUn) algorithm, which utilizes the Fisher Information Matrix (FIM) from initial unlearning models to pinpoint impacted parameters in inherited models. By employing FIM, the FIUn method breaks the sequential dependencies among the models, facilitating simultaneous unlearning and reducing computational overhead. We further design to merge disparate FIMs into a single matrix, synchronizing updates across inherited models. Experiments confirm the effectiveness of our unlearning framework. For single-class tasks, it achieves complete unlearning with 0\% accuracy for unlearned labels while maintaining 94.53\% accuracy for retained labels on average. For multi-class tasks, the accuracy is 1.07\% for unlearned labels and 84.77\% for retained labels on average. Our framework accelerates unlearning by 99\% compared to alternative methods.

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