Summary of Federated Prompt-based Decision Transformer For Customized Vr Services in Mobile Edge Computing System, by Tailin Zhou et al.
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
by Tailin Zhou, Jiadong Yu, Jun Zhang, Danny H.K. Tsang
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates providing customized virtual reality (VR) services for heterogeneous users in a mobile edge computing (MEC) system, focusing on resource allocation to maximize quality of experience (QoE). The authors introduce a QoE metric considering latency, user attention levels, and preferred resolutions. A reinforcement learning problem is formulated to learn a generalized policy applicable across diverse user environments for all MEC servers. The proposed framework employs federated learning (FL) and prompt-based sequence modeling to pre-train a common decision model across MEC servers, named FedPromptDT. FL solves the problem of insufficient local MEC data while protecting user privacy during offline training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to give people different virtual reality experiences that they’ll really enjoy using their mobile devices and computers. The goal is to make sure each person gets the best possible experience, considering things like how quickly the information is delivered, how much attention they’re giving it, and what resolution they prefer. To do this, we use a special kind of learning called reinforcement learning, which helps us figure out the best way to allocate resources so that everyone can have a great time in virtual reality. |
Keywords
» Artificial intelligence » Attention » Federated learning » Prompt » Reinforcement learning
