CAREER: Distributed Intelligence in Future Wireless Networks: From System-Aware Learning to System-Learning Co-designs

鉴于无线设备日益增长的传感和计算能力，以及对数据隐私日益关注的放大，相当数量的人工智能和机器学习任务正在分布在无线网络中的设备上运行。遗憾的是，大多数学习算法的开发很少关注底层的系统约束；而且，现有的大多数通信和网络设计很少考虑运行学习任务的独特特征。为了促进快速机器学习发展和无线网络设计之间的协同效应，该项目旨在实现分布式学习算法和无线网络的变革性联合设计。这项职业计划将进一步将教育计划与研究目标结合起来，通过i)用分布式学习组件改造现有的传感器网络课程；ii)直接让本科生和研究生参与研究，特别是来自代表性不足的群体；以及iii)外展到一般公众，特别是K-12学生和教师。对于系统感知的学习，该项目将首先开发资源高效的分布式学习算法，由此学习更新将被节俭地执行。我们将研究这些资源高效算法的稳健实现，以应对不可靠无线信道中的用户移动性和敌意攻击。关于系统学习联合设计，该项目将开发新的边学习边管理的算法，通过联合学习、电源控制、排队和工作量管理方案最大限度地提高学习精度。该项目提出了一个雄心勃勃的计划，使未来无线网络的系统学习合作设计成为可能。它的根本性进步也将影响社会技术系统，如电网、城市交通系统和水/气分配系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Given the growing sensing and computing power of wireless devices, amplified by increasing concerns on data privacy, a sizeable number of artificial intelligence and machine learning tasks are running at the devices distributed in wireless networks. Unfortunately, most of learning algorithm developments pay little attention to the underlying system constraints; and, most of existing communication and network designs rarely account for the unique characteristics of running learning tasks. To catalyze the synergies between rapid machine learning developments and the wireless network designs, this project aims at a transformative co-design of distributed learning algorithms and wireless networks. This CAREER project will further integrate an educational plan with the research goals by i) revamping the existing sensor network course with distributed learning components; ii) directly involving undergraduate and graduate students in research, especially from under-represented groups; and, iii) outreaching to the general public, in particular K-12 students and teachers.Towards this goal, this CAREER project will pursue i) system-aware learning, and ii) system-learning co-designs. For system-aware learning, the project will first develop resource-efficient distributed learning algorithms, whereby learning updates will be executed parsimoniously. Robust implementation of these resource-efficient algorithms will be studied to account for user mobility and adversarial attacks in unreliable wireless channels. Regarding system-learning co-designs, the project will develop new learning-while-managing algorithms that maximize the learning accuracy through joint learning, power control, queueing, and workload management schemes. This project presents an ambitious plan to enable system-learning co-designs of future wireless networks. Its fundamental advances will also impact sociotechnical systems, such as power grids, urban transportation systems and water/gas distribution 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.