  In this paper, we study how to optimize the federated edge learning (FEEL) in
UAV-enabled Internet of things (IoT) for B5G/6G networks, from a deep
reinforcement learning (DRL) approach. The federated learning is an effective
framework to train a shared model between decentralized edge devices or servers
without exchanging raw data, which can help protect data privacy. In
UAV-enabled IoT networks, latency and energy consumption are two important
metrics limiting the performance of FEEL. Although most of existing works have
studied how to reduce the latency and improve the energy efficiency, few works
have investigated the impact of limited batteries at the devices on the FEEL.
Motivated by this, we study the battery-constrained FEEL, where the UAVs can
adjust their operating CPU-frequency to prolong the battery life and avoid
withdrawing from federated learning training untimely. We optimize the system
by jointly allocating the computational resource and wireless bandwidth in
time-varying environments. To solve this optimization problem, we employ a deep
deterministic policy gradient (DDPG) based strategy, where a linear combination
of latency and energy consumption is used to evaluate the system cost.
Simulation results are finally demonstrated to show that the proposed strategy
outperforms the conventional ones. In particular, it enables all the devices to
complete all rounds of FEEL with limited batteries and meanwhile reduce the
system cost effectively.
