# Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching

arXiv: https://arxiv.org/abs/2501.11745 · HTML: https://mlsanity.com/paper/2501.11745/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Online Learning for Time Series Prediction](https://mlsanity.com/paper/1302.6927/) (2013)
- [Efficient Regret Minimization in Non-Convex Games](https://mlsanity.com/paper/1708.00075/) (2017)
- [LeadCache: Regret-Optimal Caching in Networks](https://mlsanity.com/paper/2009.08228/) (2020)
- [Cellular-Connected Wireless Virtual Reality: Requirements, Challenges, and Solutions](https://mlsanity.com/paper/2001.06287/) (2020)
- [Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network](https://mlsanity.com/paper/2005.08332/) (2020)
- [User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality](https://mlsanity.com/paper/2311.10645/) (2023)
- [On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey](https://mlsanity.com/paper/2402.00881/) (2024)
- [CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality](https://mlsanity.com/paper/2212.05865/) (2022)
- [Federated Multi-View Synthesizing for Metaverse](https://mlsanity.com/paper/2401.00859/) (2023)
- [Characterization of the Global Bias Problem in Aerial Federated Learning](https://mlsanity.com/paper/2212.14360/) (2022)

## Built on by (most cited first, 2 of 2)

- [Decentralized Fairness Aware Multi Task Federated Learning for VR Network](https://mlsanity.com/paper/2512.02513/) (2025)
- [A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints](https://mlsanity.com/paper/2608.26493/) (2026)

## Abstract (arXiv)

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 O(1/√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.