Machine Learning Training With Recursive Gradients for Differential Privacy
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Solution Overview
Problem
Existing machine learning models face inefficiencies and vulnerabilities to privacy attacks when trained on sensitive datasets, particularly due to the need for large batch sizes and excessive noise in stochastic gradient descent methods, which compromise computational efficiency and data privacy.
Innovation Solution
A privacy-sensitive training method using stochastic recursive gradients and varying learning rates, allowing larger batch sizes and optimal noise addition, ensuring differential privacy while maintaining robustness and accuracy through recursive gradient computations and supplementary parameter updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stochastic gradient descent with excessive noise is used to protect privacy, then privacy protection is improved, but training efficiency and model accuracy deteriorate
Solution Approach 1:
The patent introduces recursive gradient estimation as an intermediary mechanism between the training data and the model parameters. Instead of directly adding excessive noise to gradients from sensitive data, the system uses recursive gradient estimation to accumulate and smooth gradient information over multiple iterations, thereby reducing the amount of noise needed while maintaining privacy guarantees. This intermediary process allows the system to achieve differential privacy with less noise addition, improving both privacy protection and training efficiency.
Solution Approach 2:
The patent dynamically adjusts the noise scale parameter based on the recursive gradient estimation process. By changing the parameter for noise addition from a fixed large value to a dynamically determined value based on recursive gradient statistics, the system optimizes the balance between privacy protection and training efficiency. This parameter change allows adaptive noise scaling that maintains privacy while minimizing the negative impact on model convergence and accuracy.
2Productivity
If large batch sizes are used in stochastic gradient descent, then training speed is improved, but privacy protection deteriorates due to increased information leakage risk
Solution Approach 1:
The recursive gradient estimation acts as an intermediary that decouples the relationship between batch size and privacy risk. By accumulating gradient information recursively across iterations, the system can use larger batches for faster training without directly exposing individual data contributions. The recursive structure aggregates information in a way that maintains privacy guarantees even with larger batch sizes, as the cumulative effect smooths out individual data influences.
Solution Approach 2:
The patent performs preliminary recursive gradient estimation before final model updates. By pre-computing and accumulating gradient information through recursive estimation in advance of the actual parameter updates, the system can utilize larger batches efficiently while maintaining privacy. This preliminary action allows the system to prepare aggregated gradient information that is less susceptible to privacy attacks, enabling faster training with improved privacy protection.
3Reliability
If noise is added to gradients to ensure differential privacy, then privacy protection is improved, but convergence speed and model accuracy worsen
Solution Approach 1:
The patent dynamically changes the noise scale parameter based on the recursive gradient estimation process. Instead of using a fixed large noise scale that slows convergence, the system adjusts the noise parameter adaptively according to the accumulated gradient statistics. This parameter change enables the system to maintain strong differential privacy guarantees while using smaller, more appropriate noise levels at different stages of training, thereby improving convergence speed without sacrificing privacy protection.
Solution Approach 2:
The recursive gradient estimation provides a continuous accumulation of gradient information that maintains the training process's useful action despite noise addition. By continuously updating the recursive gradient estimates across iterations, the system ensures that the signal for model convergence is maintained even as noise is added for privacy. This continuity allows the training to proceed efficiently with privacy-protected gradients, reducing the time loss typically associated with noisy gradient descent.
Data Source
AI summary
System and method for privacy-sensitive training of machine learning models. The method comprises, at each of a plurality of training iterations: obtaining a respective batch of training data items that are used to determine a gradient of the objective function for the current training iteration; and updating values of the model parameters using the gradient of the objective function for the current training iteration and noise values, comprising: updating a set of supplementary values of the parameters using the gradient of the objective function for the current training iteration in accordance with a first learning rate; updating the values of the parameters using the gradient of the objective function for the current training iteration in accordance with a second learning rate; and further updating the values of the parameters by combining the values of the model parameters with the supplementary values of the parameters.