Click model has been positioned as an eﬀective approach to interpret user click behavior in search engines. Existing advances in click models mostly focus on traditional Web search which contains only ten homogeneous Web HTML documents. However, in modern commercial search engines, more and more Web search results are federated from multiple sources and contain non-HTML results returned by other heterogeneous vertical engines, such as video or image search engines. In this paper, we study user click behavior in federated search results. In order to investigate this problem, we put forward an observation that user click behavior in federated search is highly diﬀerent from that in traditional Web search, making it diﬃcult to interpret using existing click models. Thus, we propose a novel federated click model (FCM) to interpret user click behavior in federated search. In particular, we introduce two new biases in FCM. The ﬁrst indicates that users tend to be attracted by vertical results and their visual attention on them may increase the examination probability of other nearby web results. The other illustrates that user click behavior on vertical results may lead to more indication of relevance due to their presentation style in federated search. With these biases and an eﬀective model to correct them, FCM is more accurate in characterizing user click behavior in federated search. Our extensive experimental results show that FCM can outperform other click models in interpreting user click behavior in federated search and achieve signiﬁcant improvements in terms of both perplexity and log-likelihood.