Categoria:
Seminário
Onde:
Presencial
Local:
Sala de Seminários do DI e Google Meet
Descrição:
In CyberEdge networks, where federated learning (FL) orchestrates intelligence across privacy-preserving edge devices, emerging model poisoning (MP) poses a critical threat to system resilience. This talk focuses on a new type of the data-untethered attack, where an adversarial variational graph autoencoder (VGAE) constructs malicious local model updates from benign updates, bypassing access to private training data. By extracting and regenerating high-order graph structural correlations among benign client models, the new VGAE-MP attack produces stealthy and effective poisoning that evades conventional detection and leads to a progressive degradation of global model accuracy.
