  The growing literature of Federated Learning (FL) has recently inspired
Federated Reinforcement Learning (FRL) to encourage multiple agents to
federatively build a better decision-making policy without sharing raw
trajectories. Despite its promising applications, existing works on FRL fail to
I) provide theoretical analysis on its convergence, and II) account for random
system failures and adversarial attacks. Towards this end, we propose the first
FRL framework the convergence of which is guaranteed and tolerant to less than
half of the participating agents being random system failures or adversarial
attackers. We prove that the sample efficiency of the proposed framework is
guaranteed to improve with the number of agents and is able to account for such
potential failures or attacks. All theoretical results are empirically verified
on various RL benchmark tasks.
