Metaverse meets distributed machine learning: A contemporary review on the development with privacy-preserving concerns
S Gardner, Trong-Minh Hoang, Woongsoo Na, Nhu‐Ngoc Dao, Sungrae Cho
Abstract
Distributed machine learning utilization in the metaverse exposes many potential benefits. However, the combination of these advanced technologies raises significant privacy concerns due to the potential exploitation of sensitive user and system data. This paper provides a systematic investigation of over 100 recent studies across key academic databases obtained by initial keyword-filter screening followed by a thorough full-text review. Particularly, metaverse evolution and enabling infrastructure technologies are briefly summarized. Subsequently, the distributed learning architectures and their features are analyzed as well as possibly associated vulnerability discussions. Then, envisioned metaverse applications and future research challenges are highlighted before concluding remarks.