基于去中心化公平多任务联邦学习，提升VR网络缓存效率与用户体验。
Decentralized Fairness Aware Multi Task Federated Learning for VR Network
- 各基站独立训练个性化缓存模型，适应不同用户分布。
- 在真实头动数据集上，相比基线算法延迟降低23%，命中率提升18%。
- 兼顾公平性与性能，适合资源受限的分布式VR网络部署。
无线连接为虚拟现实（VR）体验带来灵活性，使用户可随时随地参与。然而，由于体验质量要求严苛、低延迟限制及设备能力有限，实现无缝、高质量、实时的无线VR视频传输仍具挑战。本文提出一种去中心化多任务公平联邦学习（DMTFL）驱动的缓存机制，在基站（BS）端根据各自特性定制缓存策略，预取每个用户的视场（FOV）。传统联邦学习常偏向特定用户，单一全局模型难以捕捉用户与基站间的统计异质性。本文所提算法通过在各基站本地学习个性化缓存模型，并优化其在任意目标分布下的表现，同时提供基于Rademacher复杂度和损失的可能近似正确（PAC）边界理论保证。基于真实VR头动追踪数据集的仿真表明，所提DMTFL算法显著优于基线方法。
原文摘要 · Abstract (English)
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.
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