Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning
arXiv:2505.12239v2 Announce Type: replace Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice. Accompanied by analytic classifiers, the PTM-based methods have achieved state-of-the-art performance in CL, in pursuit of the non-forgetting goal. Meanwhile, actively forgetting specific knowledge acquired during the CL phase is also essential in most service construction paradigms, for example, Mobile Crowd Sensing (MCS), where mobile edge nodes continuously collect sensory data and demand not only non-forgetting adaptation bu
The increasing adoption of pre-trained models in continual learning environments necessitates solutions for managing and selectively removing acquired knowledge, especially in dynamic data collection systems.
Efficient and exact forgetting services are critical for ethical AI, data privacy compliance (like 'right to be forgotten'), and maintaining model utility in real-world, data-intensive applications.
This research introduces methods for more precise and resource-efficient knowledge unlearning within complex AI models, addressing a significant practical challenge in AI deployment and governance.
- · AI developers
- · Cloud service providers
- · Mobile Crowd Sensing platforms
- · Data privacy regulators
- · Systems with inefficient forgetting mechanisms
- · AI applications lacking compliance features
AI models will become more adaptable and compliant with data governance policies, enhancing their trustworthiness and deployment scope.
The development of robust forgetting services will accelerate the adoption of continuous learning AI in sensitive sectors like healthcare and finance.
Demand for specialized AI model auditing and compliance tools will increase, leading to new service markets and regulatory considerations for 'AI forgetting'.
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