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.

随着无线网络的发展变得越来越大，传统的频谱监视方法具有基于模型的信号处理技术的发展，甚至可能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学习任务（例如图像分类）中被证明是成功的，但是对于大规模的无线传感系统而言，它几乎没有探索，这需要非常规节点分布，复杂的频道褪色和用户协作机会。该项目开发了针对大型无线系统的创新分散的异质深度学习技术。该项目的结果导致了技术创新，从而应对最先进的无线感应和管理系统的几个主要挑战，包括在超宽无线频谱环境中传统感应和管理方案无能为力，难以处理异质任务以及与深度学习的互联网络，以及对成本进行的跨越跨度的通信和计算通过开发一个革命性的分散深度学习框架来解决大型无线频谱传感的独特挑战。计划三个主要推力。 在推力1中，如今研究了复杂和大规模无线频谱感知的主要挑战，并为实用的频谱传感任务开发了创新的基于深度学习的解决方案。在推力2中，开发了专门的沟通和计算方案，以优化提议的分散深度学习框架的性能。在Thrust 3中，基于关键观察结果，即它消耗了无线网络资源来为网络资源的利用带来附加值，这是为了理解和利用机器学习在频谱管理中的复杂作用而做出的第一个探索性工作。实验测试已用于实践频谱监测应用。提出的无线传感和管理系统可以使大量的大型无线网络系统（例如5G无线网络和其他大型网状网络系统）受益。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，从而增强了现有的课程和教学法。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影响的审查标准来通过评估来支持的。

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