Yimin Chen, "Delving into differential privacy and anomaly detection: a meta-learning perspective"

In this talk, we explore security and privacy related to
meta-learning, a learning paradigm aiming to learn 'cross-task'
knowledge instead of 'single-task' knowledge. For privacy
perspective, we conjecture that meta-learning plays an important
role in future federated learning and look into federated
meta-learning systems with differential privacy design for task
privacy protection. For security perspective, we explore anomaly
detection for machine learning models. Particularly, we explore
poisoning attacks on machine learning models in which poisoning
training samples are the anomaly. Inspired from that poisoning
samples degrade trained models through overfitting, we exploit
meta-training to counteract overfitting, thus enhancing model
robustness.