Adaptive Gradient Clipping for Privacy-Preserving Model Training
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Solution Overview
Problem
Existing machine learning models trained on user data often fail to preserve user privacy, as information related to individual users can be recoverable from the trained model, compromising their privacy.
Innovation Solution
A system that trains neural networks using coordinate-wise, per-parameter adaptive clipping to determine privacy-preserving noisy gradients, which modifies gradients based on mean and standard deviation estimates, reducing noise addition while maintaining privacy, thus improving model performance and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to gradients to preserve user privacy during machine learning model training, then user privacy is maintained, but model performance and convergence speed deteriorate due to excessive noise
Solution Approach 1:
The patent applies coordinate-wise clipping that treats each gradient component independently with its own clipping threshold, rather than applying a uniform clipping level to all gradients. This local differentiation allows the system to preserve privacy while minimizing noise impact on model training by adapting the clipping strength to each specific gradient component's characteristics
Solution Approach 2:
The patent implements adaptive clipping thresholds that are dynamically adjusted during the training process based on the observed gradient statistics and convergence behavior. This dynamic adaptation allows the system to optimize the balance between privacy preservation and model performance by reducing clipping intensity as training progresses and gradients stabilize
2Ease of manufacture
If uniform clipping is applied to all gradients, then implementation is simple, but model performance deteriorates due to excessive noise addition
Solution Approach 1:
The patent replaces uniform clipping with coordinate-wise clipping that computes individual clipping thresholds for each gradient component based on its standard deviation. This approach maintains relative implementation simplicity while dramatically improving model performance by avoiding excessive noise addition to gradients that don't require strong clipping
3Productivity
If clipping thresholds are lowered to reduce noise, then model performance improves, but user privacy protection weakens
Solution Approach 1:
The patent changes the clipping threshold parameter from a fixed uniform value to a dynamic coordinate-wise value that is computed based on the standard deviation of each gradient component. This parameter transformation allows the system to achieve both privacy protection and model performance by adapting the clipping strength to the actual gradient characteristics rather than applying a conservative fixed threshold
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for privacy preserving training of a machine learning model.