Session

DescriptionWith the rapid evolution of AI technologies, ensuring robust security measures is crucial to mitigating risks and safeguarding sensitive data. This session explores cutting-edge research in AI security and privacy, addressing emerging threats and novel defenses across various machine learning paradigms. Topics include resilient federated learning on embedded devices, concealed backdoor attacks using machine unlearning, and continual novelty detection for intrusion detection systems. The session also covers secure inference of graph neural networks, privacy-preserving collaborative learning, and advancements in data-free knowledge distillation.