Output Security for Multi-user Augmented Reality using Federated Reinforcement Learning

With the rapid advancements in Augmented Reality, the number of AR users is gradually increasing and the multiuser AR ecosystem is on the rise. Currently, AR applications usually present results without limitations, which causes great latent danger to users, so it is necessary to apply strategies to ensure the safe output of AR. Due to the environmental diversities among the distributed users, the traditional approaches designed for single-user AR are not efficient for multi-user AR applications. Considering the characteristics of multi-user AR scenarios, we propose a multi-user AR output strategy model based on Federated Reinforcement Learning. With the device-fog-cloud hierarchical architecture, the proposed models are obtained first by Reinforcement Learning on users' devices, and are then hierarchically aggregated on the fog nodes and cloud server. We performed extensive AR simulations in Unity and obtained the results that show our method can avoid several security problems existent in multi-user AR applications.