  In cellular networks, resource allocation is performed in a centralized way,
which brings huge computation complexity to the base station (BS) and high
transmission overhead. This paper investigates the distributed resource
allocation scheme for cellular networks to maximize the energy efficiency of
the system in the uplink transmission, while guaranteeing the quality of
service (QoS) for cellular users. Particularly, to cope the fast varying
channels in wireless communication environment, we propose a robust federated
reinforcement learning (FRL_suc) framework to enable local users to perform
distributed resource allocation in items of transmit power and channel
assignment by the guidance of the local neural network trained at each user.
Analysis and numerical results show that the proposed FRL_suc framework can
lower the transmission overhead and offload the computation from the central
server to the local users, while outperforming the conventional multi-agent
reinforcement learning algorithm in terms of EE, and is more robust to channel
variations.
