Federated learning for the metaverse: leveraging artificial intelligence for enhanced data privacy and efficiency
Zhihao Dong, Jie Cao, Xu Zhu, Haiyong Zeng, Agbotiname Lucky Imoize · 2024
The metaverse is a virtual shared space that integrates the physical and digital worlds using technologies such as virtual reality, artificial intelligence (AI), and advanced wireless communication. It enables users to engage in diverse activities, offering experiences beyond physical constraints. AI is fundamental to the metaverse, enabling real-time decision-making and immersive experiences. However, AI model training faces challenges such as data privacy risks and high demands on radio resources. To overcome these challenges, federated learning offers a decentralized approach that enhances data privacy and reduces the need for extensive data transmission. In this chapter, we explore wireless federated learning for the metaverse and provide solutions for key issues. We analyze the factors that impact the convergence of federated learning and provide an upper bound for convergence gaps, deriving guiding principles for system design and optimization. We propose a multi-criteria client selection strategy, which significantly improves client selection efficiency. Additionally, we introduce a cooperative relaying scheme to address the straggler issue, allowing both stragglers and relays to benefit without additional burdens. To minimize the energy consumption of clients, we jointly optimize the transmission power, computing frequency, and relay-straggler associations. Simulation results demonstrate the effectiveness of the proposed methods in increasing client participation, reducing energy consumption, and enhancing model training performance. Finally, we outline some future research directions.