Abstract Gradient Training: A Unified Certification Framework for Data Poisoning, Unlearning, and Differential Privacy

This research introduces Abstract Gradient Training (AGT), a framework for verifying that machine learning models remain reliable when their training data is changed or corrupted. The framework addresses three scenarios: adversarial data poisoning (when attackers intentionally alter training samples), machine unlearning (when specific training data must be removed), and differential privacy (when individual data points are substituted). AGT works by establishing mathematical bounds on how model parameters (the internal settings that AI uses to make predictions) can change, allowing researchers to formally prove a model will behave safely despite training data perturbations.