NSF-IITP: CNS Core: Small: Federated Learning for Privacy-preserving Video Caching Network

NSF-IITP：CNS 核心：小型：隐私保护视频缓存网络的联邦学习

• 批准号: 2152646
• 负责人: Andreas Molisch
• 金额: $ 49.98万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152646&HistoricalAwards=false
• 关键词: NSF; IITP; CNS; Core; Small

Video streaming has, over the past decade, become the dominant form of entertainment, with most video clips on social media as well as movies being viewed on wireless devices. Due to large increase in demand, novel methods for content delivery must be considered so as to not overburden the wireless networks, while at the same time keeping transmission delays low, to avoid the dreaded “buffering” warning on the device of the user. One of the most promising methods for achieving this is caching at the wireless edge, i.e., storing popular content at or near the wireless base stations that users are connected to. Caching strategies usually require knowledge of the video popularity, as well as the historical preferences of the individual users. This information needs to be combined with information about the wireless network structure and network parameters, to determine what content should be cached where. Yet, in many cases it is undesirable to share detailed user profiles with the cache and/or wireless network operator. The main motivation for avoiding such sharing is user privacy. The goal of the current project is thus to develop techniques based on machine learning that enable efficient video caching and delivery systems while preserving the privacy of the users. Such algorithms will advance the state of the art in machine learning, by incorporating expert knowledge on video caching and developing new network structures derived from the specific problem. At the same time the results will benefit society by providing privacy – which is of great importance to consumers and steadily becoming more so – as well as spectral and energy efficiency of wireless networks and thus careful use of finite resources. The project will also serve to help students develop interdisciplinary thinking, and has a detailed plan for increasing the participation from underrepresented groups. The project will explore caching techniques that preserve privacy while retaining the efficiency of caching. In particular it will use Federated Learning , a form of Machine Learning that allows localized training and exchange of machine learning models, such as parameterized neural networks, between distributed nodes without requiring the exchange of underlying data such as user preferences. The developed techniques will take into account both content popularity and the state of the wireless network. Rather than separating the problem into disparate parts, such as popularity prediction on one hand, and wireless caching optimization on the other, the project will pursue an integrated approach that accounts for the interaction between all the different aspects of the work. Specific topics of investigation will include (i) techniques that do not require transmission of models to a central server, thus further strengthening privacy, (ii) algorithms for hybrid global-local models that take account of the fact that video popularity is a global descriptor while also showing some local (spatio-temporal) variations, (iii) direct learning of caching strategies without taking the detour via separate learning of video popularity and wireless network state, thus improving both efficiency and privacy. All these results will flow into an integrated, holistic system design for privacy-preserving video caching systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的十年中，视频流已成为主要的娱乐形式，社交媒体上的大多数视频剪辑以及电影都是在无线设备上观看的。由于需求大幅增长，必须考虑新颖的内容交付方法，以免无线网络负担过重，同时保持较低的传输延迟，以避免用户设备上出现可怕的“缓冲”警告。实现这一目标的最有前途的方法之一是在无线边缘进行缓存，即将流行内容存储在用户连接的无线基站处或附近。缓存策略通常需要了解视频的受欢迎程度以及个人用户的历史偏好。这些信息需要与有关无线网络结构和网络参数的信息结合起来，以确定哪些内容应该缓存在哪里。然而，在许多情况下，不希望与缓存和/或无线网络运营商共享详细的用户配置文件。 避免此类共享的主要动机是用户隐私。因此，当前项目的目标是开发基于机器学习的技术，实现高效的视频缓存和传输系统，同时保护用户的隐私。此类算法将通过结合视频缓存方面的专业知识并开发源自特定问题的新网络结构，推动机器学习领域的最新技术发展。与此同时，其结果将通过提供隐私（这对消费者来说非常重要，并且越来越重要）以及无线网络的频谱和能源效率以及谨慎使用有限资源来造福社会。该项目还将帮助学生发展跨学科思维，并制定了增加代表性不足群体参与的详细计划。该项目将探索在保留缓存效率的同时保护隐私的缓存技术。特别是，它将使用联邦学习，这是机器学习的一种形式，允许在分布式节点之间进行本地化训练和机器学习模型（例如参数化神经网络）的交换，而无需交换用户偏好等底层数据。所开发的技术将考虑内容流行度和无线网络的状态。该项目不会将问题分成不同的部分，例如一方面进行流行度预测，另一方面进行无线缓存优化，而是采用一种集成方法来考虑工作的所有不同方面之间的交互。 研究的具体主题将包括（i）不需要将模型传输到中央服务器的技术，从而进一步增强隐私性，（ii）混合全局局部模型的算法，该算法考虑到视频流行度是全局描述符，同时也显示一些局部（时空）变化，（iii）直接学习缓存策略，而不需要通过单独学习视频流行度和无线网络状态来绕道，从而改善两者 效率和隐私。所有这些结果都将流入隐私保护视频缓存系统的集成、整体系统设计中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。