arXiv:2602.00567v2 Announce Type: replace Abstract: The deployment of quantized neural networks on edge devices, combined with privacy regulations like GDPR, creates an urgent need for machine unlearning in quantized models. However, existing methods face critical challenges: they induce forgetting by training models to memorize incorrect labels, conflating forgetting with misremembering, and employ scalar gradient reweighting that cannot resolve directional conflicts between gradients. We propose OEU, a novel Orthogonal Entropy Unlearning framework with two key innovations: 1) Entropy-guided
The increasing deployment of AI on edge devices and stringent privacy regulations like GDPR are pushing the need for robust machine unlearning in quantized models.
This research addresses a critical technical challenge for privacy-preserving AI on constrained hardware, which is essential for broad AI adoption in sensitive applications.
The ability to more effectively 'unlearn' data from quantized neural networks improves privacy compliance and model adaptability, particularly for edge AI.
- · Edge AI developers
- · Privacy-focused tech companies
- · Healthcare sector
- · Finance sector
- · Companies with poor data governance
- · Legacy unlearning methods
Improved deployability of AI models on edge devices with strong privacy guarantees.
Reduced regulatory hurdles for AI applications dealing with personal or sensitive data.
Acceleration of sovereign AI initiatives by providing tools for domestic control over data and models, even after deployment.
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Read at arXiv cs.LG