  The recent advancement of Deep Reinforcement Learning (DRL) contributed to
robotics by allowing automatic controller design. The automatic controller
design is a crucial approach for designing swarm robotic systems, which require
more complex controllers than a single robot system to lead a desired
collective behaviour. Although the DRL-based controller design method showed
its effectiveness, the reliance on the central training server is a critical
problem in real-world environments where robot-server communication is unstable
or limited. We propose a novel Federated Learning (FL) based DRL training
strategy (FLDDPG) for use in swarm robotic applications. Through the comparison
with baseline strategies under a limited communication bandwidth scenario, it
is shown that the FLDDPG method resulted in higher robustness and
generalisation ability into a different environment and real robots, while the
baseline strategies suffer from the limitation of communication bandwidth. This
result suggests that the proposed method can benefit swarm robotic systems
operating in environments with limited communication bandwidth, e.g., in
high-radiation, underwater, or subterranean environments.
