Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
CoRR(2024)
Abstract
This paper investigates resource allocation to provide heterogeneous users
with customized virtual reality (VR) services in a mobile edge computing (MEC)
system. We first introduce a quality of experience (QoE) metric to measure user
experience, which considers the MEC system's latency, user attention levels,
and preferred resolutions. Then, a QoE maximization problem is formulated for
resource allocation to ensure the highest possible user experience,which is
cast as a reinforcement learning problem, aiming to learn a generalized policy
applicable across diverse user environments for all MEC servers. To learn the
generalized policy, we propose a framework that employs federated learning (FL)
and prompt-based sequence modeling to pre-train a common decision model across
MEC servers, which is named FedPromptDT. Using FL solves the problem of
insufficient local MEC data while protecting user privacy during offline
training. The design of prompts integrating user-environment cues and
user-preferred allocation improves the model's adaptability to various user
environments during online execution.
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