MLWiNS: Decentralized Heterogeneous Deep Learning for Efficient Wireless Spectrum Monitoring

MLWiNS：用于高效无线频谱监控的去中心化异构深度学习

• 批准号: 2003211
• 负责人: Xiang Chen
• 金额: $ 50万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003211&HistoricalAwards=false
• 关键词: MLWiNS; Decentralized; Heterogeneous; Deep; Learning

As wireless networks evolve to be increasingly massive and complex, traditional spectrum monitoring methods with model-based signal processing techniques have become inadequate and may even fail to provide accurate wireless network evaluation. Meanwhile, deep learning techniques have been proven successful in standard centralized learning tasks (e.g., image classification), yet it is barely explored for large-scale wireless sensing systems, which entail unconventional node distribution, complex channel fading and user collaboration opportunities. This project develops innovative decentralized heterogeneous deep learning techniques for large-scale wireless systems. The outcomes of this project lead to technical innovations that tackle several major challenges of the state-of-the-art wireless sensing and management systems, including the incapability of conventional sensing and management schemes in ultra-wide wireless spectrum settings, the difficulty in handling heterogeneous tasks and non-IID data with deep learning technologies, as well as the costly overhead of communication and computation in distributed deep learning for large-scale networks.This project addresses the unique challenges of large-scale wireless spectrum sensing by developing a revolutionary decentralized deep learning framework. Three main thrusts are planned. In Thrust 1, major challenges of complex and large-scale wireless spectrum sensing nowadays are investigated, and an innovative deep learning-based solution is developed for practical spectrum sensing tasks. In Thrust 2, dedicated communication and computation schemes are developed to optimize the performance of the proposed decentralized deep learning framework. In Thrust 3, the very first exploratory effort is made to understand and utilize the intricate role of machine learning in spectrum management, based on the key observation that it consumes wireless network resources to bring in added value to network resource utilization. Experimental testing is demonstrated for practical spectrum monitoring applications. The proposed wireless sensing and management system can benefit a plethora of large-scale wireless network systems, such as a 5G wireless network and other large-scale mesh networking systems. The education plan enhances existing curricula and pedagogy by integrating interdisciplinary modules on embedded systems, mobile computing, and machine learning with newly developed teaching practices.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.

随着无线网络的日益庞大和复杂，传统的基于模型的信号处理技术的频谱监测方法已经显得力不从心，甚至无法提供准确的无线网络评估。同时，深度学习技术在标准的集中式学习任务(如图像分类)中已被证明是成功的，但它在大规模无线传感系统中几乎没有被探索，这需要非常规的节点分布、复杂的信道衰落和用户协作机会。该项目为大规模无线系统开发了创新的分散式、异构式深度学习技术。该项目的成果导致了技术创新，以应对最先进的无线感知和管理系统的几个主要挑战，包括传统的感知和管理方案在超宽带无线频谱环境中的无能为力，使用深度学习技术处理异质任务和非IID数据的困难，以及大规模网络的分布式深度学习中昂贵的通信和计算开销。该项目通过开发一个革命性的分散式深度学习框架来解决大规模无线频谱感知的独特挑战。计划了三个主要的推进项目。在推力1中，研究了当今复杂和大规模无线频谱感知面临的主要挑战，并为实际频谱感知任务开发了一种创新的基于深度学习的解决方案。在推力2中，开发了专用的通信和计算方案来优化所提出的去中心化深度学习框架的性能。在推力3中，第一次探索性的努力是理解和利用机器学习在频谱管理中的复杂作用，基于机器学习消耗无线网络资源来为网络资源利用带来附加值这一关键观察。对实际的频谱监测应用进行了实验测试。提出的无线传感和管理系统可以惠及过多的大规模无线网络系统，如5G无线网络和其他大规模网状网络系统。该教育计划通过将嵌入式系统、移动计算和机器学习的跨学科模块与新开发的教学实践相结合，增强了现有的课程和教学方法。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

Efficient Distributed Swarm Learning for Edge Computing

• DOI: 10.1109/icc45041.2023.10279508
• 发表时间: 2023-05
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Xin Fan;Yue Wang;Yan Huo;Zhi Tian

Fed2: Feature-Aligned Federated Learning

• DOI: 10.1145/3447548.3467309
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Fuxun Yu;Weishan Zhang;Zhuwei Qin;Zirui Xu;Di Wang;Chenchen Liu;Zhi Tian;Xiang Chen

CB-DSL: Communication-Efficient and Byzantine-Robust Distributed Swarm Learning on Non-i.i.d. Data

• DOI: 10.1109/tccn.2023.3312345
• 发表时间: 2022-08
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: Xin Fan;Yue Wang;Yan Huo;Zhi Tian

BEV-SGD: Best Effort Voting SGD against Byzantine Attacks for Analog Aggregation based Federated Learning Over the Air

BEV-SGD：针对基于模拟聚合的空中联邦学习的拜占庭攻击的尽力投票 SGD

• DOI: 10.1109/jiot.2022.3164339
• 发表时间: 2022
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Xin Fan;Yue Wang;Yan Huo;Zhi Tian
• 通讯作者: Zhi Tian

DQC-ADMM: Decentralized Dynamic ADMM With Quantized and Censored Communications

DQC-ADMM：具有量化和审查通信的去中心化动态 ADMM

• DOI: 10.1109/tnnls.2021.3051638
• 发表时间: 2021
• 期刊: IEEE Transactions on Neural Networks and Learning Systems
• 影响因子: 10.4
• 作者: Liu, Yaohua;Wu, Gang;Tian, Zhi;Ling, Qing
• 通讯作者: Ling, Qing