Practical AI Solutions for Efficient Data Condensation
Introduction
As data continues to grow, the need for efficient data condensation is crucial. Practical solutions are needed to address privacy concerns and optimize model performance while minimizing storage and computational costs.
Solution: Dyn-PSG
A new approach, Dyn-PSG, proposes a dynamic differential privacy-based dataset condensation method. By dynamically adjusting gradient clipping thresholds and sensitivity measures, Dyn-PSG reduces noise during training while maintaining strict privacy guarantees. This results in improved accuracy compared to existing methods, making it a superior approach for efficient data condensation.
Key Features of Dyn-PSG
- Dynamic Clipping Threshold: Instead of using a fixed norm, Dyn-PSG gradually adjusts clipping thresholds during training to reduce noise in later stages.
- Dynamic Sensitivity: Adapts sensitivity measures based on the maximum norm observed in per-example gradients, ensuring noise is not unnecessarily injected.
- Noise Injection: Injects noise based on maximum gradient size after clipping to mitigate accuracy loss and parameter instability.
Evaluation and Results
Extensive experiments on benchmark datasets demonstrated that Dyn-PSG outperformed existing approaches in accuracy while maintaining privacy guarantees. It effectively balances data utility and privacy.
AI Integration for Business Enhancement
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