Trust-Worthy Semantic Communications for the Metaverse Relying on Federated Learning

TL;DR

This paper proposes a trust-worthy semantic communication system for the Metaverse using federated learning to ensure privacy and security while managing multi-modal data on resource-limited edge devices.

Contribution

It introduces a novel federated learning-based framework for secure, privacy-preserving semantic communication tailored for the Metaverse environment.

Findings

Federated learning enhances privacy in semantic communication.

The system supports multi-modal data processing in resource-constrained devices.

Identifies key research directions and open issues in the field.

Abstract

As an evolving successor to the mobile Internet, the Metaverse creates the impression of an immersive environment, integrating the virtual as well as the real world. In contrast to the traditional mobile Internet based on servers, the Metaverse is constructed by billions of cooperating users by harnessing their smart edge devices having limited communication and computation resources. In this immersive environment an unprecedented amount of multi-modal data has to be processed. To circumvent this impending bottleneck, low-rate semantic communication might be harnessed in support of the Metaverse. But given that private multi-modal data is exchanged in the Metaverse, we have to guard against security breaches and privacy invasions. Hence we conceive a trust-worthy semantic communication system for the Metaverse based on a federated learning architecture by exploiting its distributed decision-making and privacy-preserving capability. We conclude by identifying a suite of promising research directions and open issues.