Federated Reinforcement Learning Based on Multi-head Attention Mechanism for Vehicle Edge Caching

Abstract

Vehicles request road condition information, traffic information, and various audio-visual entertainment frequently. Repeat Download will burden the core network and seriously affect the user experience. Edge caching is a promising technology that can effectively alleviate the pressure of repeatedly downloading content from the cloud. There are many existing edge cache scheduling methods, but they all have limitations. his paper proposes an edge cache scheduling method based on the multi-head attention mechanism federal reinforcement learning (FRLMA). Firstly, the problem is modeled as a Markov decision model. The local models are trained through a deep reinforcement learning method. Finally, the federated reinforcement learning framework of edge Cooperative Cache is established. In particular, the multi-head attention mechanism is introduced to weigh the contribution of the local model to the global model from multiple angles. Simulation results show that the FRLMA method has better convergence and is superior to the most current popular methods in terms of hit rate and average delay.