Federated Unlearning (FU) has emerged as a promising solution to respond to “the right to be forgotten” of clients, by allowing clients to erase their data from global models without compromising model performance. Unfortunately, researchers find that the parameter variations of models induced by FU expose clients’ data information, enabling attackers to infer the label of unlearning data, while label inference attacks against FU remain unexplored. In this paper, we introduce and analyze a new privacy threat against FU and propose a novel label inference attack, \(\texttt{ULIA}\) , which can infer unlearning data labels across three FU levels. To address the unique challenges of inferring labels via the models variations, we design a gradient-label mapping mechanism in \(\texttt{ULIA}\) that establishes a relationship between gradient variations and unlearning labels, enabling inferring labels on accumulated model variations. We evaluate \(\texttt{ULIA}\) on both IID and non-IID settings. Experimental results show that in the IID setting, \(\texttt{ULIA}\) achieves a \(100\%\) Attack Success Rate (ASR) under both class-level and client-level unlearning. Even when only \(1\%\) of a user’s local data is forgotten, \(\texttt{ULIA}\) still attains an ASR ranging from \(93\%\) to \(62.3\%\) .