CAREER: Towards a Communication Foundation for Distributed and Decentralized Machine Learning

职业：为分布式和去中心化机器学习建立通信基础

基本信息

批准号：
2143559
负责人：
Cong Shen
金额：
$ 50万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2027-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143559&HistoricalAwards=false
关键词：
CAREER Towards Communication Foundation Distributed

项目摘要

With the emerging paradigm shift towards moving the data collection and machine learning (ML) model training to the edge, distributed and decentralized ML has become increasingly critical to empowering many applications, such as autonomous driving, recommender systems, and Internet of Things (IoT). This trend imposes formidable challenges on the underlying communication design and catalyzes its evolution from connecting people and connecting things to connecting intelligence. This CAREER project develops fundamental communication technologies to enable distributed and decentralized ML in next-generation wireless systems. It transforms wireless communications from pure data transfer to intelligence transfer, building a synergy between communications and ML in a closely integrated fashion. In partnership with industry, results enabled by this project can be prototyped and integrated into real systems, potentially impacting 6G standardization and other future communication systems. The cross disciplinary nature of this project naturally translates into case studies and new development in a number of undergraduate and graduate level courses, by integrating ML and AI to the curriculum of communications and networking. The education and outreach activities will collectively promote a common thread of providing the best opportunities for diverse groups of bright young minds to develop into future scientists and engineers.This project aims at developing the theoretical foundation and novel communication algorithms for distributed and decentralized ML, thereby catalyzing a paradigm shift of wireless communications towards connecting intelligence. Towards this end, this project will develop a novel random orthogonalization principle that tightly integrates physical layer communications with ML. Additionally, the research will study the impact of fading and noisy channels on the performance of ML, and design communication methods to improve the accuracy and convergence of the ML tasks. Finally, a novel adaptive communication method for distributed and decentralized multi-armed bandits will be investigated, where coding and interleaving designs for online learning with adversarial communications will be studied. The proposed research promotes the fundamental understanding of the synergy between distributed and decentralized ML and communications, and will have broad applications beyond the specific problems studied in this project.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.

随着新兴范式转向将数据收集和机器学习（ML）模型培训升级到边缘，分布式和分散的ML对于增强许多应用程序的能力越来越重要，例如自动驾驶，推荐系统和物联网（IoT）（IoT）。这一趋势对基础沟通设计构成了巨大的挑战，并促进了其从连接人员和将事物连接到连接智能的发展。该职业项目开发了基本的通信技术，以在下一代无线系统中启用分布式和分散的ML。它将无线通信从纯数据传输转变为智能传输，以紧密整合的方式在通信和ML之间建立协同作用。与行业合作，可以将该项目启用的结果原型并集成到实际系统中，从而可能影响6G标准化和其他未来的通信系统。该项目的跨学科性质自然地转化为案例研究和许多本科和研究生级课程的新发展，通过将ML和AI集成到通信和网络课程中。教育和宣传活动将共同促进一个共同的线索，为各种聪明的年轻人群体提供最佳机会，以发展为未来的科学家和工程师。该项目旨在为分布式和分散的ML开发理论基础和新颖的交流算法催化无线通信向连接智能的范式转移。为此，该项目将开发出一种新颖的随机正交原理，该原理将物理层与ML紧密整合在一起。此外，该研究将研究褪色和嘈杂渠道对ML性能的影响，以及设计沟通方法，以提高ML任务的准确性和收敛性。最后，将研究一种用于分布式和分散的多军匪徒的新型自适应通信方法，其中将研究用于在线学习的编码和交错设计，并研究对抗性通信。拟议的研究促进了对分布式和分散的ML和通信之间协同作用的基本理解，并将在本项目研究中提出的广泛应用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的评估来支持的。智力优点和更广泛的影响审查标准。