Label theft in vertical federated learning now leaves a readable signal: the server spots it in embeddings it already holds
Active label inference in vertical federated learning leaves a class-separability anomaly in the adversary's embeddings, letting the server trigger a defense that acts on the adversary alone and pushes label inference below what honest training already leaks on CIFAR-10, CIFAR-100 and CINIC-10, at under two points of task-accuracy cost.