  Split learning is a popular technique used for vertical federated learning
(VFL), where the goal is to jointly train a model on the private input and
label data held by two parties. This technique uses a split-model, trained
end-to-end, by exchanging the intermediate representations (IR) of the inputs
and gradients of the IR between the two parties. We propose ExPLoit - a
label-leakage attack that allows an adversarial input-owner to extract the
private labels of the label-owner during split-learning. ExPLoit frames the
attack as a supervised learning problem by using a novel loss function that
combines gradient-matching and several regularization terms developed using key
properties of the dataset and models. Our evaluations show that ExPLoit can
uncover the private labels with near-perfect accuracy of up to 99.96%. Our
findings underscore the need for better training techniques for VFL.
