标题： 面向使用无人机群进行内容分发的联邦多臂老虎机学习 显示英文标题

标题： Towards Federated Multi-Armed Bandit Learning for Content Dissemination using Swarm of UAVs

摘要： 本文介绍了一种适用于在各种灾难场景中通信隔离的用户社区进行关键内容访问的无人机启用的内容管理架构。 所提出的架构利用固定锚点无人机和移动微型无人机的混合网络进行无处不在的内容分发。 锚点无人机配备有垂直和水平通信链路，并为本地用户提供服务，而移动微型渡运无人机则通过增加移动性扩展到不同社区的覆盖范围。 重点是开发一种动态学习最优缓存策略的内容分发系统，以最大化内容可用性。 核心创新是一种基于分布式联邦多臂老虎机学习的自适应内容分发框架。 目标是根据地理时间内容流行度和用户需求变化优化无人机内容缓存决策。 还引入了一种选择性缓存算法，通过结合无人机间的信息共享来减少冗余内容复制。 该方法在保持用户偏好独特性的同时，整合了分布式学习系统中的智能。 这种方法提高了学习算法适应不同用户偏好的能力。 功能验证和性能评估证实了所提出的架构在不同网络规模、无人机群和内容流行度模式中的实用性。 显示英文摘要

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.