Collaborative Research: CNS Core: Medium: Towards Federated Learning over 5G Mobile Devices: High Efficiency, Low Latency, and Good Privacy

最近出现的联邦学习（FL）允许分布式数据源协作训练全局模型，而无需共享其隐私敏感的原始数据。然而，由于深度学习模型的巨大规模，模型的下载和更新会产生大量的网络流量，这给现有的电信基础设施带来了巨大的负担。该项目将FL over 5G移动的设备作为解决这一困境的可行应用场景，这将显著改善FL over 5G移动的设备的设计，分析和实现。研究成果将大大丰富机器学习技术和5G系统及其他领域的知识。而且，这个项目是多学科的，涉及机器学习/深度学习/联邦学习、边缘计算、无线通信和网络、安全和隐私、计算机架构设计等，这将成为一个富有成效的培训基地，为研究生和本科生提供多学科技能，为未来的劳动力提供支持，促进国家经济发展。此外，对高中生的外展活动将增加女性和少数民族学生在科学和工程领域的参与。具体而言，通过观察迭代模型更新往往表现出高稀疏性，研究人员利用模型更新稀疏性来设计模型修剪和量化方案，以优化本地训练和隐私保护模型更新，从而降低能耗和模型更新流量。他们通过进行四项研究任务来实现这一设计目标：（1）根据本地数据和模型稀疏性，在单个5G移动终端内的FL中设计软硬件协同设计的模型修剪方案和自适应量化技术，以减少本地计算和内存访问;（2）在“工作”（即，本地计算）和“通话”（即，（3）开发基于稀疏化特性和量化适应性的新型差分隐私压缩方案，以严格保护数据隐私，同时保持FL中的高模型精度和通信效率;以及（4）该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

Recent emerging federated learning (FL) allows distributed data sources to collaboratively train a global model without sharing their privacy sensitive raw data. However, due to the huge size of the deep learning model, the model downloads and updates generate significant amount of network traffic which exerts tremendous burden to existing telecommunication infrastructure. This project takes FL over 5G mobile devices as a workable application scenario to address this dilemma, which will significantly improve the design, analysis and implementation of FL over 5G mobile devices. The research outcomes will substantially enrich the knowledge of machine learning technologies and 5G systems and beyond. Moreover, this project is multidisciplinary, involving machine learning/deep learning/federated learning, edge computing, wireless communications and networking, security and privacy, computer architectural design, etc., which will serve as a fruitful training ground for both graduate and undergraduate students to equip them with multidisciplinary skills for future work force to boost the national economy. Furthermore, outreach activities to high school students will increase the participation of female and minority students in science and engineering.Specifically, by observing that iterative model updates tend to show high sparsity, the investigators leverage model update sparsity to design model pruning and quantization schemes to optimize local training and privacy-preserving model updating in order to lower both energy consumption and model update traffic. They achieve this design goal by conducting the four research tasks: (1) designing software-hardware co-designed model pruning schemes and adaptive quantization techniques in FL within a single 5G mobile device according to the local data and model sparsity property to reduce the local computation and memory access; (2) making sound trade-off between "working" (i.e., local computing) and "talking" (i.e., 5G wireless transmissions) to boost the overall energy/communications efficiency for FL over 5G mobile devices; (3) developing novel differentially private compression schemes based on sparsification property and quantization adaptability to rigorously protect data privacy while maintaining high model accuracy and communication efficiency in FL; and (4) building a testbed to thoroughly evaluate the proposed designs.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.