Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

Researchers propose Analytic Continual Unlearning (ACU), a gradient-free method enabling efficient removal of specific knowledge from pre-trained models during continuous learning phases while preserving privacy. The approach uses closed-form solutions to handle sequential forgetting requests, addressing gaps in existing unlearning techniques that struggle with privacy violations and adversarial request patterns.

The emergence of Continual Unlearning (CU) represents a critical intersection of machine learning efficiency and data privacy regulation. Traditional continual learning methods focus on preventing catastrophic forgetting—retaining previously learned knowledge—but increasingly face demands to selectively erase specific information, particularly in privacy-sensitive applications like mobile edge computing and crowd-sensing systems. This tension grows sharper as regulatory frameworks like GDPR establish legal rights to data erasure that must operate within dynamic learning environments.

The ACU methodology addresses fundamental limitations in existing unlearning approaches by leveraging analytical rather than gradient-based solutions. This design choice offers computational advantages while maintaining theoretical exactness in the forgetting process. The gradient-free architecture proves particularly valuable because gradient-based retraining typically requires access to historical training data—a privacy violation itself—making ACU's approach fundamentally more privacy-preserving by design.

For practitioners deploying machine learning in regulated environments, this research enables privacy compliance without sacrificing model performance. The technique's compatibility with both sample-level and class-level unlearning requests broadens its applicability across diverse use cases. Mobile edge deployments, federated learning systems, and privacy-critical applications benefit from reduced computational overhead and elimination of data retention requirements.

The importance of this work extends beyond academic validation. As machine learning systems become embedded in consumer-facing applications, the ability to efficiently and provably remove specific information becomes a competitive and legal necessity. Organizations deploying pre-trained models in continual learning scenarios now have a scalable mechanism to honor erasure requests without retraining entire models, reducing operational costs while ensuring compliance.

• →ACU enables privacy-preserving knowledge removal from continuously learning models without accessing historical training data.
• →Gradient-free analytical solutions reduce computational overhead compared to existing unlearning methods.
• →The approach handles both sample-level and class-level forgetting requests, increasing practical applicability.
• →Sequential unlearning capabilities address adversarial or frequent erasure request scenarios in edge computing systems.
• →Closed-form solutions guarantee exact forgetting while maintaining model fidelity on retained knowledge.