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
基本信息
- 批准号:2106761
- 负责人:
- 金额:$ 25万
- 依托单位:
- 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:
- 关键词:
项目摘要
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
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Hybrid Local SGD for Federated Learning with Heterogeneous Communications
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Yuanxiong Guo;Ying Sun;Rui Hu;Yanmin Gong
- 通讯作者:Yuanxiong Guo;Ying Sun;Rui Hu;Yanmin Gong
Energy-Efficient Distributed Machine Learning at Wireless Edge with Device-to-Device Communication
- DOI:10.1109/icc45855.2022.9838508
- 发表时间:2022-05
- 期刊:
- 影响因子:0
- 作者:Rui Hu;Yuanxiong Guo;Yanmin Gong
- 通讯作者:Rui Hu;Yuanxiong Guo;Yanmin Gong
Concentrated Differentially Private Federated Learning With Performance Analysis
- DOI:10.1109/ojcs.2021.3099108
- 发表时间:2021
- 期刊:
- 影响因子:5.9
- 作者:Rui Hu;Yuanxiong Guo;Yanmin Gong
- 通讯作者:Rui Hu;Yuanxiong Guo;Yanmin Gong
Scalable and Low-Latency Federated Learning With Cooperative Mobile Edge Networking
- DOI:10.1109/tmc.2022.3216837
- 发表时间:2022-05
- 期刊:
- 影响因子:7.9
- 作者:Zhenxiao Zhang;Zhidong Gao;Yuanxiong Guo;Yanmin Gong
- 通讯作者:Zhenxiao Zhang;Zhidong Gao;Yuanxiong Guo;Yanmin Gong
Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability
- DOI:10.1109/jiot.2022.3158895
- 发表时间:2022-09-15
- 期刊:
- 影响因子:10.6
- 作者:Chen, Rui;Li, Liang;Pan, Miao
- 通讯作者:Pan, Miao
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Yuanxiong Guo其他文献
Coalitional Datacenter Energy Cost Optimization in Electricity Markets
电力市场中的联合数据中心能源成本优化
- DOI:
10.1145/3077839.3077860 - 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Zhenjian Yu;Yuanxiong Guo;M. Pan - 通讯作者:
M. Pan
Practical Collaborative Learning for Crowdsensing in the Internet of Things with Differential Privacy
具有差异隐私的物联网中群体感知的实用协作学习
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Yuanxiong Guo;Yanmin Gong - 通讯作者:
Yanmin Gong
A stochastic game approach to cyber-physical security with applications to smart grid
网络物理安全的随机博弈方法及其在智能电网中的应用
- DOI:
10.1109/infcomw.2018.8406833 - 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Yuanxiong Guo;Yanmin Gong;Laurent L. Njilla;Charles A. Kamhoua - 通讯作者:
Charles A. Kamhoua
CrossFuser: Multi-Modal Feature Fusion for End-to-End Autonomous Driving Under Unseen Weather Conditions
CrossFuser:多模态特征融合,实现未见天气条件下的端到端自动驾驶
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Weishang Wu;Xiaoheng Deng;Ping Jiang;Shaohua Wan;Yuanxiong Guo - 通讯作者:
Yuanxiong Guo
Beef Up the Edge: Spectrum-Aware Placement of Edge Computing Services for the Internet of Things
增强边缘:物联网边缘计算服务的频谱感知布局
- DOI:
10.1109/tmc.2018.2883952 - 发表时间:
2019-12 - 期刊:
- 影响因子:7.9
- 作者:
Haichuan Ding;Yuanxiong Guo;Xuanheng Li;Yuguang Fang - 通讯作者:
Yuguang Fang
Yuanxiong Guo的其他文献
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{{ truncateString('Yuanxiong Guo', 18)}}的其他基金
Collaborative Research:CISE-MSI:DP:CNS:Enabling On-Demand and Flexible Mobile Edge Computing with Integrated Aerial-Ground Vehicles
合作研究:CISE-MSI:DP:CNS:通过集成空地车辆实现按需且灵活的移动边缘计算
- 批准号:
2318663 - 财政年份:2023
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
Collaborative Research: CISE-MSI: DP: RI: Towards Scalable, Resilient and Robust Foraging with Heterogeneous Robot Swarms
合作研究:CISE-MSI:DP:RI:利用异构机器人群实现可扩展、有弹性和稳健的觅食
- 批准号:
2318683 - 财政年份:2023
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
RAPID: Collaborative: Location Privacy Preserving COVID-19 Symptom Map Construction via Mobile Crowdsourcing for Proactive Constrained Resource Allocation
RAPID:协作:通过移动众包构建位置隐私保护 COVID-19 症状图,以实现主动的受限资源分配
- 批准号:
2029685 - 财政年份:2020
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
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