Label Inference Attack against Split Learning under Regression Setting

Understand

As a crucial building block in vertical Federated Learning (vFL), Split Learning (SL) has demonstrated its practice in the two-party model training collaboration, where one party holds the features of data samples and another party holds the corresponding labels.

• Such method is claimed to be private considering the shared information is only the embedding vectors and gradients instead of private raw data and labels.
• However, some recent works have shown that the private labels could be leaked by the gradients.
• These existing attack only works under the classification setting where the private labels are discrete.