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

鉴于无线设备的传感和计算能力不断增长，加上对数据隐私的日益关注，大量人工智能和机器学习任务正在无线网络中分布的设备上运行。不幸的是，大多数学习算法的发展很少关注底层系统的约束条件，并且，大多数现有的通信和网络设计很少考虑运行学习任务的独特特性。为了促进快速机器学习开发和无线网络设计之间的协同作用，该项目旨在实现分布式学习算法和无线网络的变革性协同设计。这个CAREER项目将进一步整合教育计划与研究目标，通过i）修改现有的传感器网络课程与分布式学习组件; ii）直接参与研究的本科生和研究生，特别是来自代表性不足的群体;以及，iii）外展服务予公众，特别是幼稚园至12年级的学生和教师。为达致这个目标，这个CAREER项目将追求i）系统感知学习，ii）系统学习协同设计。对于系统感知学习，该项目将首先开发资源高效的分布式学习算法，从而节省地执行学习更新。这些资源有效的算法的鲁棒实现将被研究，以考虑用户的移动性和对抗性攻击在不可靠的无线信道。关于系统学习协同设计，该项目将开发新的边学习边管理算法，通过联合学习、功率控制、共享和工作负载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，以实现未来无线网络的系统学习协同设计。该奖项反映了NSF的法定使命，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。

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.