In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge distillation (KD) driven training framework for FL is proposed, where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset. To overcome the challenge of train the big teacher model in resource limited user devices, the digital twin (DT) is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources. Then, during model distillation, each user can update the parameters of its model at either the physical entity or the digital agent. The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming (MIP) problem. To solve the problem, Q-learning and optimization are jointly used, where Q-learning selects models for users and determines whether to train locally or on the server, and optimization is used to allocate resources for users based on the output of Q-learning. Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.

翻译：针对联邦学习系统中的异构性问题，本文提出了一种知识蒸馏驱动的联邦学习训练框架。在该框架中，每个用户可根据需求自主选择神经网络模型，并利用私有数据集从大型教师模型中蒸馏知识。为解决资源受限用户设备上训练大型教师模型的挑战，本文利用数字孪生技术，将教师模型部署在服务器端具备充足计算资源的数字孪生体中进行训练。在模型蒸馏阶段，每个用户可在物理实体或数字代理处更新模型参数。用户侧模型选择、训练卸载与资源分配的联合问题被建模为混合整数规划问题。为求解该问题，本文联合使用Q学习与优化方法：Q学习负责为用户选择模型并决定训练是在本地执行还是卸载至服务器，优化方法则基于Q学习输出结果为用户分配资源。仿真结果表明，所提出的数字孪生辅助知识蒸馏框架及联合优化方法能显著提升用户平均准确率，同时降低总延迟。