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

在过去的十年里，视频流已经成为主要的娱乐形式，社交媒体上的大多数视频片段以及在无线设备上观看的电影。由于需求的大量增加，必须考虑新的内容传输方法，以便不使无线网络负担过重，同时保持低传输延迟，以避免用户设备上可怕的“缓冲”警告。实现这一目标的最有希望的方法之一是在无线边缘进行缓存，即在用户连接的无线基站附近或附近存储流行内容。缓存策略通常需要了解视频的流行程度，以及单个用户的历史偏好。此信息需要与有关无线网络结构和网络参数的信息相结合，以确定应该在何处缓存哪些内容。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。避免这种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，在保护用户隐私的同时，实现高效的视频缓存和传输系统。这些算法将通过整合视频缓存方面的专家知识和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发展。与此同时，这些成果将通过提供隐私——这对消费者来说非常重要，而且越来越重要——以及无线网络的频谱和能源效率，从而谨慎地利用有限的资源，从而造福社会。该项目还将帮助学生培养跨学科思维，并为增加代表性不足群体的参与制定了详细计划。该项目将探索在保持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，这是一种机器学习形式，允许在分布式节点之间进行本地化训练和交换机器学习模型，如参数化神经网络，而不需要交换用户偏好等底层数据。所开发的技术将同时考虑到内容的流行程度和无线网络的状态。该项目将采用一种集成的方法，考虑工作中所有不同方面之间的交互，而不是将问题分成不同的部分，例如一方面是流行预测，另一方面是无线缓存优化。具体的研究主题将包括(i)不需要将模型传输到中央服务器的技术，从而进一步加强隐私；（ii）考虑到视频流行度是一个全局描述符，同时也显示了一些局部（时空）变化的混合全局-局部模型算法；（iii）直接学习缓存策略，而无需通过单独学习视频流行度和无线网络状态绕路。从而提高效率和隐私。所有这些结果将流入一个集成的，整体的系统设计的隐私保护视频缓存系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.