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

Shunpu Tang, Wenqi Zhou, Lunyuan Chen, Lijia Lai, Junjuan Xia, Liseng Fan

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.

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