The federated learning paradigm allows several data owners to contribute to a machine learning task without exposing their potentially sensitive data. We focus on cumulative reward maximization in Multi-Armed Bandits (MAB), a classical reinforcement learning model for decision making under uncertainty. We demonstrate Samba, a generic framework for Secure federAted Multi-armed BAndits. The demonstration platform is a Web interface that simulates the distributed components of Samba, and which helps the data scientist to configure the end-to-end workflow of deploying a federated MAB algorithm. The user-friendly interface of Samba, allows the users to examine the interaction between three key dimensions of federated MAB: cumulative reward, computation time, and security guarantees. We demonstrate Samba with two real-world datasets: Google Local Reviews and Steam Video Game.