# Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System

arXiv: https://arxiv.org/abs/2402.09729 · HTML: https://mlsanity.com/paper/2402.09729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [Prompting Decision Transformer for Few-Shot Policy Generalization](https://mlsanity.com/paper/2206.13499/) (2022)
- [Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems](https://mlsanity.com/paper/1901.06455/) (2019)
- [Federated Reinforcement Learning with Environment Heterogeneity](https://mlsanity.com/paper/2204.02634/) (2022)
- [Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee](https://mlsanity.com/paper/2110.14074/) (2021)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data](https://mlsanity.com/paper/2211.09299/) (2022)
- [A Survey on Transformers in Reinforcement Learning](https://mlsanity.com/paper/2301.03044/) (2023)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning](https://mlsanity.com/paper/2206.10185/) (2022)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services](https://mlsanity.com/paper/2208.05438/) (2022)
- [Enabling Long-term Fairness in Dynamic Resource Allocation](https://mlsanity.com/paper/2208.05898/) (2022)
- [FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features](https://mlsanity.com/paper/2308.15786/) (2023)
- [Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1712.08266/) (2017)
- [Clinical Decision Transformer: Intended Treatment Recommendation through Goal Prompting](https://mlsanity.com/paper/2302.00612/) (2023)
- [Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videos](https://mlsanity.com/paper/2003.00429/) (2020)
- [Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2305.07845/) (2023)
- [Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality](https://mlsanity.com/paper/2305.08569/) (2023)
- [Bi-directional Digital Twin and Edge Computing in the Metaverse](https://mlsanity.com/paper/2211.08700/) (2022)
- [Reliability Enhancement for VR Delivery in Mobile-Edge Empowered Dual-Connectivity Sub-6 GHz and mmWave HetNets](https://mlsanity.com/paper/2011.10293/) (2020)
- [FoVR: Attention-based VR Streaming through Bandwidth-limited Wireless Networks](https://mlsanity.com/paper/2209.15198/) (2022)

## Built on by (most cited first, 2 of 2)

- [Federated Large Language Models: Current Progress and Future Directions](https://mlsanity.com/paper/2409.15723/) (2024)
- [Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models](https://mlsanity.com/paper/2502.07211/) (2025)

## Abstract (arXiv)

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.