Wireless connectivity promises to unshackle virtual reality (VR) experiences, allowing users to engage from anywhere, anytime. However, delivering seamless, high-quality, real-time VR video wirelessly is challenging due to the stringent quality of experience requirements, low latency constraints, and limited VR device capabilities. This paper addresses these challenges by introducing a novel decentralized multi task fair federated learning (DMTFL) based caching that caches and prefetches each VR user's field of view (FOV) at base stations (BSs) based on the caching strategies tailored to each BS. In federated learning (FL) in its naive form, often biases toward certain users, and a single global model fails to capture the statistical heterogeneity across users and BSs. In contrast, the proposed DMTFL algorithm personalizes content delivery by learning individual caching models at each BS. These models are further optimized to perform well under any target distribution, while providing theoretical guarantees via Rademacher complexity and a probably approximately correct (PAC) bound on the loss. Using a realistic VR head-tracking dataset, our simulations demonstrate the superiority of our proposed DMTFL algorithm compared to baseline algorithms.

翻译：无线连接有望解放虚拟现实（VR）体验，使用户能够随时随地参与。然而，由于严格的体验质量要求、低延迟约束以及有限的VR设备能力，通过无线方式提供无缝、高质量、实时的VR视频具有挑战性。本文通过引入一种新颖的基于去中心化多任务公平联邦学习（DMTFL）的缓存机制来解决这些挑战，该机制根据为每个基站（BS）定制的缓存策略，在基站处缓存和预取每个VR用户的视场（FOV）。在原始形式的联邦学习（FL）中，通常偏向某些用户，单一的全局模型无法捕捉用户和基站之间的统计异质性。相比之下，所提出的DMTFL算法通过在每个基站学习个体缓存模型来个性化内容交付。这些模型进一步优化，以在任何目标分布下表现良好，同时通过Rademacher复杂度和损失的概率近似正确（PAC）边界提供理论保证。使用真实的VR头部跟踪数据集，我们的仿真证明了所提出的DMTFL算法相较于基线算法的优越性。