Digital Twin Enhanced Federated Reinforcement Learning With Lightweight Knowledge Distillation in Mobile Networks

The high-speed mobile networks offer great potentials to many future intelligent applications, such as autonomous vehicles in smart transportation systems. Such networks provide the possibility to interconnect mobile devices to achieve fast knowledge sharing for efficient collaborative learning and operations, especially with the help of distributed machine learning, e.g., Federated Learning (FL), and modern digital technologies, e.g., Digital Twin (DT) systems. Typically, FL requires a fixed group of participants that have Independent and Identically Distributed (IID) data for accurate and stable model training, which is highly unlikely in real-world mobile network scenarios. In this paper, in order to…

• NN
  National Natural Science Foundation of China

  Awards: 62072171, 72091515
• JS
  Japan Society for the Promotion of Science

  Award: 23K11064