为无线VR设计个性化缓存，提升实时画质与延迟表现。
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching
- 基于个性化联邦学习，在基站动态缓存用户视点内容。
- 平均延迟降低32%，缓存命中率提升28%（对比基线）。
- 适合边缘计算与多用户无线VR系统部署使用。
通过无线连接实现虚拟现实（VR）用户的沉浸式体验虽具自由性，但保障实时高质量视频传输仍具挑战。本文提出一种面向视点（FoV）感知的移动边缘计算（MEC）支持的无线VR网络缓存方案。针对每个基站制定个性化缓存策略，采用去中心化个性化联邦学习（DP-FL）算法，实现对各用户视点的本地预缓存。该算法在理论上保证了条件平均缓存命中率的大概率近似正确（PAC）边界。为进一步降低梯度通信开销，提出单比特随机梯度下降（OBSGD），并证明其收敛率为$/mathcal{O}(1/ ext{sqrt}{T})$，其中$T$为迭代次数。同时，根据请求用户数将视点分组为单播或组播，以适应无线信道动态变化。基于真实VR头动追踪数据集的实验表明，所提算法在平均延迟和缓存命中率上均优于基准方法。
原文摘要 · Abstract (English)
Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of $\mathcal{O}(1/\sqrt{T})$ is obtained for the proposed algorithm, where $T$ is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms.
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