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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Metaverse Technology

Background:

  • The peer-to-peer (P2P) decentralized gaming industry is evolving towards creating realistic gaming environments (GE) for game players (GPs).
  • Metaverse innovations are driving the gaming industry beyond augmented reality (AR) and virtual reality (VR) to enhance virtual game worlds.
  • Gaming metaverses (GMs) enable GPs to interact, socialize, and trade virtual items within the GE, with game servers (GSs) analyzing data for personalization.

Purpose of the Study:

  • To propose a novel scheme, Game-o-Meta, that integrates federated learning (FL) into the gaming environment (GE) to address challenges in decentralized gaming metaverses (GMs).
  • To enhance the realism of virtual game worlds by improving GE support for game players (GPs) and ensuring data security and privacy.
  • To overcome the limitations of traditional centralized game servers (GSs), such as high latency, bandwidth constraints, and data security vulnerabilities.

Main Methods:

  • Proposed the Game-o-Meta scheme, integrating federated learning (FL) where game player (GP) data is trained locally on devices, not on centralized game servers (GSs).
  • Envisioned the gaming environment (GE) utilizing sixth-generation (6G) tactile internet services to tackle bandwidth and latency issues, enabling real-time haptic control.
  • Implemented a process where GP game tasks are collected and initially trained on the GS, followed by GPs downloading a pre-trained model for local data training.

Main Results:

  • The proposed Game-o-Meta scheme demonstrated viability in enhancing decentralized gaming environments.
  • Evaluated performance based on key parameters including GP task offloading efficiency, GP avatar rendering latency, and GS availability.
  • The results indicated significant improvements compared to traditional centralized schemes, highlighting the effectiveness of the federated learning approach.

Conclusions:

  • The Game-o-Meta scheme effectively addresses latency, bandwidth, and data security concerns in gaming metaverses (GMs) by leveraging federated learning (FL) and 6G tactile internet.
  • Federated learning in the gaming environment (GE) allows for personalized experiences while ensuring game player (GP) data privacy through local training.
  • The proposed approach represents a significant advancement in creating more realistic, secure, and responsive decentralized gaming experiences.