Artificial intelligence and robotics are advancing at an incredible pace; however, there is a risk associated with the data privacy and personal information of users interacting with these systems and platforms. In this context, the federated learning approach emerged to enable large-scale, distributed learning without the need to transmit or store any information necessary to train the learning models. In a previous paper, we presented a system capable of detecting, locating, and classifying what kind of contact occurs between humans and one of our robots using innovative contact microphone technology. In this work we go further, improving the previously presented touch system with a multi-user, multi-robot, distributed, and scalable learning approach that is able to learn in a collaborative and incremental way while respecting the privacy of the user’s information. The system has been successfully evaluated in a real environment with 28 different users divided in 7 different groups. To assess the performance of our system with this federated learning approach, we compared it to the same distributed learning system without federated learning. That is, the control group for this comparison is a central node directly receiving all the training examples obtained by each robot locally. We found that in this context the inclusion of federated learning improves the results concerning traditional distributed learning.