Parallel Unlearning in Inherited Model Networks

Understand

Unlearning is challenging in generic learning frameworks with the continuous growth and updates of models exhibiting complex inheritance relationships.

• This paper presents a novel unlearning framework that enables fully parallel unlearning among models exhibiting inheritance.
• We use a chronologically Directed Acyclic Graph (DAG) to capture various unlearning scenarios occurring in model inheritance networks.
• Central to our framework is the Fisher Inheritance Unlearning (FIUn) method, designed to enable efficient parallel unlearning within the DAG.