面向无人机群内容分发的联邦多臂老虎机学习研究
Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs
AI总结：
针对灾害场景下通信隔离用户的关键内容访问需求，提出无人机群混合网络架构，结合联邦多臂老虎机学习与选择性缓存算法优化缓存策略，提升内容可用性并验证其在多场景下的有效性。
摘要：
本文提出一种无人机赋能的内容管理架构，适用于各类灾害场景下通信隔离用户群体的关键内容访问需求。该架构利用固定锚点无人机与移动微型无人机的混合网络实现泛在内容分发：锚点无人机配备垂直与横向通信链路，服务本地用户；移动微型摆渡无人机则凭借高机动性扩展社区间覆盖范围。研究聚焦于开发动态学习最优缓存策略以最大化内容可用性的分发系统，核心创新是基于分布式联邦多臂老虎机学习的自适应框架，可根据时空内容流行度与用户需求变化优化缓存决策；同时引入选择性缓存算法，通过无人机间信息共享减少冗余复制，既保留用户偏好独特性又融合分布式学习智能，提升算法适应多样化偏好的能力。功能验证与性能评估表明，该架构在不同网络规模、无人机群及内容流行度模式下均有效。
英文摘要：
This paper introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical content access in communities of users that are communication-isolated during diverse types of disaster scenarios. The proposed architecture leverages a hybrid network of stationary anchor UAVs and mobile Micro-UAVs for ubiquitous content dissemination. The anchor UAVs are equipped with both vertical and lateral communication links, and they serve local users, while the mobile micro-ferrying UAVs extend coverage across communities with increased mobility. The focus is on developing a content dissemination system that dynamically learns optimal caching policies to maximize content availability. The core innovation is an adaptive content dissemination framework based on distributed Federated Multi-Armed Bandit learning. The goal is to optimize UAV content caching decisions based on geo-temporal content popularity and user demand variations. A Selective Caching Algorithm is also introduced to reduce redundant content replication by incorporating inter-UAV information sharing. This method strategically preserves the uniqueness in user preferences while amalgamating the intelligence across a distributed learning system. This approach improves the learning algorithm's ability to adapt to diverse user preferences. Functional verification and performance evaluation confirm the proposed architecture's utility across different network sizes, UAV swarms, and content popularity patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
