Multi-view spectral clustering (MVSC) has become a popular approach to harvest knowledge about group information from multiple views of data, owned by different parties. A high quality MVSC approach usually requires collecting massive amount of data from each view party, in order to perform MVSC algorithm in a centralized manner. However, such centralized MVSC approach raises serious privacy concerns, not only in terms of the sensitivity property of many real-world data such as medical or financial records, but also in terms of the regulations from authorities to preclude centralized operations. Hence it is crucial to design new paradigm for training spectral clustering model on multi-view data in industrial scenarios. In this article, we propose a distributed and secure framework named Federated Multi-view Spectral Clustering (FMSC), in which a group of view parties collaboratively perform a MVSC model, but couldn't learn the data of other participants. FMSC is inspired by the concept of federated learning, and utilize Homomorphic Encryption (HE) and Differential Privacy (DP) to achieve secure and private clustering. We conduct a series of extensive experiments to verify the effectiveness of FMSC on both synthetic and real-world datasets. Evaluations show that FMSC achieves respectable clustering results over conventional centralized approaches.