  In this paper, we show how the Federated Learning (FL) framework enables
learning collectively from distributed data in connected robot teams. This
framework typically works with clients collecting data locally, updating neural
network weights of their model, and sending updates to a server for aggregation
into a global model. We explore the design space of FL by comparing two
variants of this concept. The first variant follows the traditional FL approach
in which a server aggregates the local models. In the second variant, that we
call Flow-FL, the aggregation process is serverless thanks to the use of a
gossip-based shared data structure. In both variants, we use a data-driven
mechanism to synchronize the learning process in which robots contribute model
updates when they collect sufficient data. We validate our approach with an
agent trajectory forecasting problem in a multi-agent setting. Using a
centralized implementation as a baseline, we study the effects of staggered
online data collection, and variations in data flow, number of participating
robots, and time delays introduced by the decentralization of the framework in
a multi-robot setting.
