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In this paper, we study how to optimize the federated edge learning (FEEL) inUAV-enabled Internet of things (IoT) for B5G/6G networks, from a deepreinforcement learning (DRL) approach. The federated learning is an effectiveframework to train a shared model between decentralized edge devices or serverswithout exchanging raw data, which can help protect data privacy. InUAV-enabled IoT networks, latency and energy consumption are two importantmetrics limiting the performance of FEEL. Although most of existing works havestudied how to reduce the latency and improve the energy efficiency, few workshave investigated the impact of limited batteries at the devices on the FEEL.Motivated by this, we study the battery-constrained FEEL, where the UAVs canadjust their operating CPU-frequency to prolong the battery life and avoidwithdrawing from federated learning training untimely. We optimize the systemby jointly allocating the computational resource and wireless bandwidth intime-varying environments. To solve this optimization problem, we employ a deepdeterministic policy gradient (DDPG) based strategy, where a linear combinationof latency and energy consumption is used to evaluate the system cost.Simulation results are finally demonstrated to show that the proposed strategyoutperforms the conventional ones. In particular, it enables all the devices tocomplete all rounds of FEEL with limited batteries and meanwhile reduce thesystem cost effectively.

Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks