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

协作研究：CNS 核心：中：迈向 5G 移动设备上的联邦学习：高效率、低延迟和良好的隐私性

• 批准号: 2107057
• 负责人: Miao Pan
• 金额: $ 50万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107057&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

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.

最近的新兴联合学习（FL）允许分布式数据源可以协作训练全球模型，而无需共享其隐私敏感的原始数据。但是，由于深度学习模型的范围很大，该模型下载和更新产生了大量的网络流量，这给现有的电信基础架构带来了巨大负担。该项目将FL超过5G移动设备作为解决这一难题的可行应用程序方案，这将大大改善FL在5G移动设备上的设计，分析和实施。研究成果将大大丰富机器学习技术和5G系统及以后的知识。此外，该项目是多学科的，涉及机器学习/深度学习/联合学习，边缘计算，无线通信以及网络，安全和隐私，计算机建筑设计等，这将为研究生和本科学生提供富有成果的培训地，以使他们拥有多学科技能，以使未来的工作力量增强民族经济。此外，向高中生的外展活动将增加女性和少数族裔学生参与科学和工程的参与。特别是，通过观察到迭代模型更新倾向于显示高稀疏性，研究人员利用模型更新模型来设计模型预处理和量化计划，以优化本地培训和私密模型的模型更新，以降低能源消费和模型的更新，以更新和模型更新。他们通过执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属性，设计软件硬件共同设计的模型修剪方案和自适应量化技术在单个5G移动设备中的自适应量化技术，以减少本地计算和内存访问； （2）在“工作”（即本地计算）和“说话”（即5G无线传输）之间进行合理的权衡，以提高5G移动设备的FL的总体能量/通信效率； （3）基于稀疏属性和量化适应性来制定新颖的私人压缩方案，以严格保护数据隐私，同时维持FL中的高模型准确性和沟通效率； （4）构建一个测试台以彻底评估拟议的设计。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知识分子优点和更广泛的影响审查标准来评估的。

项目成果

EEFL: High-Speed Wireless Communications Inspired Energy Efficient Federated Learning over Mobile Devices

• DOI: 10.1145/3581791.3596865
• 发表时间: 2023-06
• 期刊: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services
• 作者: Rui Chen;Qiyu Wan;Xinyue Zhang;Xiaoqi Qin;Yanzhao Hou;D. Wang;Xin Fu;Miao Pan

Energy Efficient Federated Learning Over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission

• DOI: 10.1109/tmc.2022.3213766
• 发表时间: 2020-12
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Rui Chen;Liang Li;Kaiping Xue;Chi Zhang;Miao Pan;Yuguang Fang

To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices

• DOI: 10.1109/globecom46510.2021.9685793
• 发表时间: 2021-11
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM)
• 作者: Pavana Prakash;Jiahao Ding;Maoqiang Wu;M. Shu;Rong Yu;M. Pan

To Talk or to Work: Dynamic Batch Sizes Assisted Time Efficient Federated Learning Over Future Mobile Edge Devices

• DOI: 10.1109/twc.2022.3189320
• 发表时间: 2022-12-01
• 期刊: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
• 影响因子: 10.4
• 作者: Shi, Dian;Li, Liang;Han, Zhu
• 通讯作者: Han, Zhu

BS-pFL: Enabling Low-Cost Personalized Federated Learning by Exploring Weight Gradient Sparsity

• DOI: 10.1109/ijcnn55064.2022.9892137
• 发表时间: 2022-07
• 期刊: 2022 International Joint Conference on Neural Networks (IJCNN)
• 作者: Lening Wang;Manojna Sistla;Mingsong Chen;Xin Fu