  Deep learning methods have revolutionized mobile robotics, from advanced
perception models for an enhanced situational awareness to novel control
approaches through reinforcement learning. This paper explores the potential of
federated learning for distributed systems of mobile robots enabling
collaboration on the Internet of Robotic Things. To demonstrate the
effectiveness of such an approach, we deploy wheeled robots in different indoor
environments. We analyze the performance of a federated learning approach and
compare it to a traditional centralized training process with a priori
aggregated data. We show the benefits of collaborative learning across
heterogeneous environments and the potential for sim-to-real knowledge
transfer. Our results demonstrate significant performance benefits of FL and
sim-to-real transfer for vision-based navigation, in addition to the inherent
privacy-preserving nature of FL by keeping computation at the edge. This is, to
the best of our knowledge, the first work to leverage FL for vision-based
navigation that also tests results in real-world settings.
