Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network
Yejun He, Mengna Yang, Zhou He, Mohsen Guizani
Abstract
Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.