Privacy-Aware Lifelong Learning

• URL: http://arxiv.org/abs/2505.10941v1
• Date: Fri, 16 May 2025 07:27:00 GMT
• Title: Privacy-Aware Lifelong Learning
• Authors: Ozan Özdenizci, Elmar Rueckert, Robert Legenstein,
• Abstract summary: The field of machine unlearning focuses on explicitly forgetting certain previous knowledge from pretrained models when requested.<n>We propose a solution, privacy-aware lifelong learning (PALL), involving optimization of task-specific sparseworks with parameter sharing within a single architecture.<n>We empirically demonstrate the scalability of PALL across various architectures in image classification, and provide a state-of-the-art solution.
• Score: 14.83033354320841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lifelong learning algorithms enable models to incrementally acquire new knowledge without forgetting previously learned information. Contrarily, the field of machine unlearning focuses on explicitly forgetting certain previous knowledge from pretrained models when requested, in order to comply with data privacy regulations on the right-to-be-forgotten. Enabling efficient lifelong learning with the capability to selectively unlearn sensitive information from models presents a critical and largely unaddressed challenge with contradicting objectives. We address this problem from the perspective of simultaneously preventing catastrophic forgetting and allowing forward knowledge transfer during task-incremental learning, while ensuring exact task unlearning and minimizing memory requirements, based on a single neural network model to be adapted. Our proposed solution, privacy-aware lifelong learning (PALL), involves optimization of task-specific sparse subnetworks with parameter sharing within a single architecture. We additionally utilize an episodic memory rehearsal mechanism to facilitate exact unlearning without performance degradations. We empirically demonstrate the scalability of PALL across various architectures in image classification, and provide a state-of-the-art solution that uniquely integrates lifelong learning and privacy-aware unlearning mechanisms for responsible AI applications.

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