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.

最近出现的联合学习(FL)允许分布式数据源在不共享隐私敏感原始数据的情况下协作训练全局模型。然而，由于深度学习模型的庞大规模，模型的下载和更新产生了大量的网络流量，给现有的电信基础设施带来了巨大的负担。本项目将FL over 5G移动设备作为一个可行的应用场景来解决这一困境，这将显著改善FL over 5G移动设备的设计、分析和实现。研究成果将极大地丰富机器学习技术和5G系统等方面的知识。此外，这个项目是多学科的，涉及机器学习/深度学习/联合学习、边缘计算、无线通信和网络、安全和隐私、计算机体系结构设计等，这将成为研究生和本科生富有成效的培训基地，为未来的劳动力培养多学科技能，促进国民经济发展。此外，针对高中生的外展活动将增加女性和少数族裔学生对科学和工程的参与。具体地说，通过观察迭代模型更新往往表现出高度稀疏性，调查人员利用模型更新稀疏性来设计模型剪枝和量化方案，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。他们通过四个研究任务来实现这一设计目标：(1)根据本地数据和模型的稀疏性，在单个5G移动设备内设计软硬件共同设计的FL模型剪枝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)和谈话(即5G无线传输)之间进行合理的权衡，以提高FL在5G移动设备上的整体能量/通信效率；(3)基于稀疏化特性和量化适应性开发新的差分私有压缩方案，以严格保护数据隐私，同时保持FL的高模型精度和通信效率；以及(4)建立试验台以彻底评估所提出的设计。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。

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

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

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

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

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