Gradient Mirage: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning

Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface is a faithful derivative of the client's full-label training objective. This gradient-objective consistency allows a curious server to recover priv...