# Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN

arXiv: https://arxiv.org/abs/2606.26123 · HTML: https://mlsanity.com/paper/2606.26123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 8 of 8)

- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks](https://mlsanity.com/paper/2206.11328/) (2022)
- [New Challenges in Reinforcement Learning: A Survey of Security and Privacy](https://mlsanity.com/paper/2301.00188/) (2022)
- [Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing](https://mlsanity.com/paper/1906.04594/) (2019)
- [PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications](https://mlsanity.com/paper/2204.12064/) (2022)
- [Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach](https://mlsanity.com/paper/2201.11281/) (2022)
- [DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks](https://mlsanity.com/paper/2605.23056/) (2026)

## Abstract (arXiv)

Ultra-low latency and high throughput are required for Virtual Reality (VR) services in 6G networks, which presents critical challenges for Software-Defined Radio Access Networks (SD-RANs) dynamic resource management. This work propose a mobility-driven, privacy-aware Multi-Agent Reinforcement Learning (MARL) framework for VR slice management, in which cooperative agents maximize resource distribution over end-to-end VR links while protecting the privacy of user data. Our approach incorporates mobility prediction and an information bottleneck encoder to facilitate effective and secure agent collaboration. In simulations, comparisons with traditional methods are studied which show up to 34% throughput improvement, 28% fewer resources, and 85% less privacy leakage, guaranteeing dependable immersive VR experiences in future 6G environments.