Neural Network Unlearning with Selective Retraining Batches
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Solution Overview
Problem
Conventional machine learning models trained on sensitive personal data face challenges in efficiently handling data deletion requests without complete retraining, leading to high retraining costs and inefficiencies, and existing unlearning algorithms are not generalizable across domains.
Innovation Solution
The use of noisy stochastic gradient descent (SGD) and optimal transport algorithms to determine equivalence conditions, allowing selective retraining of neural networks based on insertion or deletion requests, thereby avoiding full retraining when conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete retraining is performed to remove sensitive personal data, then data privacy requirements are satisfied, but retraining time and computational costs increase significantly
Solution Approach 1:
The patent segments the retraining process into selective batches rather than complete retraining. Only specific training batches containing the sensitive data are retrained, while other batches are reused from the pre-trained model. This segmentation principle allows the system to satisfy privacy requirements by removing sensitive data while avoiding the time cost of complete retraining.
Solution Approach 2:
The patent applies partial action by performing unlearning on only the necessary portions of the training data (specific batches containing sensitive personal data) rather than the entire dataset. This partial retraining approach maintains privacy compliance while significantly reducing computational time and resources compared to full retraining.
2Measurement precision
If complete retraining is performed to handle data deletion requests, then data accuracy is maintained, but computational resources and costs increase
Solution Approach 1:
The training dataset is divided into multiple batches, and the patent identifies and retrains only those specific batches that contain the deleted sensitive data. This segmentation allows the system to maintain model accuracy on affected data while conserving computational resources by reusing gradient information from unchanged batches.
Solution Approach 2:
The patent recovers and reuses gradient computations from training batches that are not affected by data deletion. Instead of discarding all gradient information and performing complete retraining, the system recovers usable gradient data from unaffected batches and combines it with newly computed gradients from batches containing deleted data, thereby reducing computational resource consumption.
3Ease of manufacture
If existing unlearning algorithms are used, then data removal is achieved, but the algorithms are not generalizable across different domains
Solution Approach 1:
The patent develops a universal unlearning framework based on stochastic gradient descent that can be applied across multiple domains and task types. The method uses general mathematical principles (gradient computation, batch segmentation, equivalence conditions) that are domain-agnostic, allowing the same unlearning algorithm to be applied to different machine learning tasks and data types while maintaining efficiency.
Data Source
AI summary
Systems and methods for training a neural network are described. One or more embodiments of the present disclosure include training a neural network based on a first combined gradient of a loss function at a plurality of sampled elements of a dataset; receiving an insertion request that indicates an insertion element to be added to the dataset, or a deletion request that indicates a deletion element to be removed from the dataset, wherein the deletion element is one of the plurality of sampled elements; computing a second combined gradient of the loss function by adding the insertion element to the dataset or by replacing the deletion element with a replacement element from the dataset; determining whether the first combined gradient and the second combined gradient satisfy a stochastic condition; and retraining the neural network to obtain a modified neural network based on the determination.