Efficient Federated Learning for Metaverse via Dynamic User Selection, Gradient Quantization and Resource Allocation

Metaverse is envisioned to merge the actual world with a virtual world to bring users unprecedented immersive feelings. To ensure user experience, federated learning (FL) has been expected as a critical enabler to provide metaverse users with high-quality sensing, communicating, and rendering. However, considering the limitation of wireless communication resources and the stringent requirements of users, collaborating with massive metaverse users to realize FL still has tremendous challenges. Most pioneer works on improving the performance of FL assume that the system states are static, which is unsuitable in the metaverse. Because the FL in the metaverse is always a complicated long-term iteration process, where the fluctuations of channel status and available computing resources of users are inevitable, a changeless strategy may lead to poor results. Therefore, this paper proposes an efficient FL scheme relying on dynamic user selection, gradient quantization, and resource allocation. Specifically, we derive the convergence error bound to reveal the impact of user selection, wireless transmission error, and gradient quantization error of each iteration on FL's convergence. Based on the theoretical analysis, we jointly and dynamically optimize the user selection, gradient quantization, and resource allocation to minimize the error bound with time and energy consumption budgets. Furthermore, to make the formulated sequential decision-making problem tractable, we transform it into a Markov decision process and design a soft actor-critic-based solution. Extensive experiments validate that our proposed scheme has superior performance compared to conventional schemes in dynamic-changing network environments.