  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.
