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.

随着无线网络发展到越来越大规模和复杂，传统的基于模型的信号处理技术的频谱监测方法已经变得不充分，甚至可能无法提供准确的无线网络评估。与此同时，深度学习技术已被证明在标准的集中式学习任务中是成功的（例如，图像分类），但它几乎没有探索大规模的无线传感系统，这需要非常规的节点分布，复杂的信道衰落和用户协作的机会。该项目为大规模无线系统开发创新的分散式异构深度学习技术。该项目的成果带来了技术创新，解决了最先进的无线传感和管理系统的几个主要挑战，包括超宽无线频谱设置中传统传感和管理方案的无能，处理异构任务的困难和深度学习技术的非IID数据，以及大规模网络分布式深度学习中昂贵的通信和计算开销。该项目通过开发革命性的去中心化深度学习框架来解决大规模无线频谱感知的独特挑战。三个主要的推进计划。 在Thrust 1中，研究了当今复杂和大规模无线频谱感知的主要挑战，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Thrust 2中，开发了专用的通信和计算方案，以优化所提出的分散式深度学习框架的性能。在Thrust 3中，第一个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，基于它消耗无线网络资源为网络资源利用带来附加值的关键观察。实验测试证明了实际的频谱监测应用。所提出的无线感测和管理系统可以有益于大量的大规模无线网络系统，诸如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、移动的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价值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