Conformal Machine Unlearning via Adaptive Model Decomposition
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Solution Overview
Problem
Current methods for data removal from machine learning models are inefficient, resource-intensive, and compromise model performance and regulatory compliance, leading to inconsistencies and reduced trustworthiness.
Innovation Solution
Employ a conformal-based sub-model selection technique combined with an adaptive decomposition process to strategically decompose models into subunits tailored to data reliability, allowing precise and targeted data removal while maintaining model integrity and predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete model retraining is performed to remove data, then data removal is achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent segments the model into multiple sub-models, each trained on specific portions of the data. This allows targeted retraining of only the affected sub-models rather than the entire model, significantly reducing computational resources and time while maintaining effective data removal.
Solution Approach 2:
The patent extracts and removes specific data points from the training set and corresponds to their removal in the model by retraining only the affected sub-models. This extraction approach eliminates the need to retrain the complete model, addressing the computational efficiency issue.
2Reliability
If complete model retraining is performed to remove data, then data removal is achieved, but processing time is excessively increased
Solution Approach 1:
The model is divided into multiple sub-models that can be independently retrained. When data removal is required, only the relevant sub-models are retrained, dramatically reducing processing time compared to retraining the entire model while maintaining effective data removal.
Solution Approach 2:
The patent extracts the affected sub-models from the larger model system and retrains only those specific portions. This extraction strategy minimizes processing time by avoiding unnecessary retraining of unaffected model components.
3Adaptability or versatility
If data removal is performed to ensure privacy compliance, then regulatory compliance is improved, but model performance and trustworthiness are compromised
Solution Approach 1:
By segmenting the model into sub-models, the patent enables selective data removal from specific sub-models while preserving the integrity and performance of other sub-models. This maintains overall model performance and trustworthiness while achieving privacy compliance.
Solution Approach 2:
The patent applies local quality by allowing different parts of the model (sub-models) to have different data compositions. This enables tailored data removal strategies that maintain model performance in unaffected regions while ensuring compliance in affected regions.
4Device complexity
If traditional data removal methods are used, then simplicity is maintained, but consistency and trustworthiness of model outputs are reduced
Solution Approach 1:
The patent introduces segmentation of the model into sub-models, which increases structural complexity but enables consistent and trustworthy model outputs by allowing targeted, controlled data removal that preserves model integrity and performance.
Data Source
AI summary
A method for removing data from a model includes identifying removal data for removal from the model, where the model includes sub-models. The method also includes identifying a sub-model from the sub-models associated with the removal data, where the sub-model includes conformal predictors. Further, the method includes performing a data exclusion action on the sub-model to obtain a modified sub-model and making a determination that the modified sub-model is above a threshold accuracy based on a reevaluation using a first validation data set. In addition, the method includes calibrating, based on the determination, the conformal predictors of the modified sub-model to obtain calibrated conformal predictors. Moreover, the method includes validating the calibrated conformal predictors using a second validation data set and reintegrating, based on the validating, the modified sub-model into the model.