面向元宇宙的基于联邦学习的可信语义通信
Trust-Worthy Semantic Communications for the Metaverse Relying on Federated Learning
AI总结：
针对元宇宙中多模态数据处理瓶颈及隐私安全挑战，提出基于联邦学习架构的可信语义通信系统，利用分布式决策与隐私保护能力，并展望未来研究方向。
摘要：
作为移动互联网的演进后继者，元宇宙营造出沉浸式环境的体验，将虚拟世界与现实世界融为一体。与传统基于服务器的移动互联网不同，元宇宙由数十亿合作用户利用其通信和计算资源有限的智能边缘设备构建而成。在这种沉浸式环境中，需要处理前所未有的多模态数据。为规避这一迫在眉睫的瓶颈，可借助低速率语义通信来支持元宇宙。但由于元宇宙中交换的是私有多模态数据，我们必须防范安全漏洞和隐私侵犯。因此，我们基于联邦学习架构，利用其分布式决策和隐私保护能力，为元宇宙构想了一种可信的语义通信系统。最后，我们指出了一系列有前景的研究方向和开放问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
