In the context of edge computing environments in general and the metaverse in particular, federated learning (FL) has emerged as a distributed machine learning paradigm that allows multiple users to collaborate on training a shared machine learning model locally, eliminating the need for uploading raw data to a central server. It is perhaps the only training paradigm that preserves the privacy of user data, which is essential for computing environments as personal as the metaverse. However, the original FL architecture proposed is not scalable to a large number of user devices in the metaverse community. To mitigate this problem, hierarchical federated learning (HFL) has been introduced as a general distributed learning paradigm, inspiring a number of research works. In this paper, we present several types of HFL architectures, with a special focus on the three-layer client-edge-cloud HFL architecture, which is most pertinent to the metaverse due to its delay-sensitive nature. We also examine works that take advantage of the natural layered organization of three-layer client-edge-cloud HFL to tackle some of the most challenging problems in FL within the metaverse. Finally, we outline some future research directions of HFL in the metaverse.

With the rapid development of immersive multimedia technologies, 360-degree video services have quickly gained popularity and how to ensure sufficient spatial presence of end users when viewing 360-degree videos becomes a new challenge. In this regard, accurately acquiring users’ sense of spatial presence is of fundamental importance for video service providers to improve their service quality. Unfortunately, there is no efficient evaluation model so far for measuring the sense of spatial presence for 360-degree videos. In this paper, we first design an assessment framework to clarify the influencing factors of spatial presence. Related parameters of 360-degree videos and head-mounted display devices are both considered in this framework. Well-designed subjective experiments are then conducted to investigate the impact of various influencing factors on the sense of presence. Based on the subjective ratings, we propose a spatial presence assessment model that can be easily deployed in 360-degree video applications. To the best of our knowledge, this is the first attempt in literature to establish a quantitative spatial presence assessment model by using technical parameters that are easily extracted. Experimental results demonstrate that the proposed model can reliably predict the sense of spatial presence.