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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

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Model-Based Reinforcement Learning for Quantized Federated L...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

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arXiv 2023年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L. Zheng, Huihuo Robinson, Richard D. Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Northwestern University EvanstonIL United States Department of Materials Science and Engineering University of California at Berkeley BerkeleyCA United States Department of Chemistry and Chemical Biology Cornell University IthacaNY United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Cornell University IthacaNY United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY United States Dow Chemical Co. MidlandMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials. © 2023, CC BY.

关键词： Zinc sulfide

Ensuring Privacy and Correlation Awareness in Multi-Dimensional Service Quality Prediction and Recommendation for Iot

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SSRN

SSRN 2024年

作者： Zhong, Weiyi Fang, Wei Zhao, Yifan Wang, Sifeng Yan, Chao Jiang, Rong Khan, Maqbool Yang, Xuan Rafique, Wajid Qufu Normal University School of Engineering Rizhao276800 China Qufu Normal University School of Computer Science Rizhao276800 China Weifang University of Science and Technology Weifang262700 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650000 China Software Competence Center Hagenberg Upper Austria 4232 Austria Angeles University Foundation Pampanga2009 Philippines University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada

Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and wise medicine. While several challenges are raised when we fuse the multiple-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named TLTM) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. At last, to validate the effectiveness and efficiency of the TLTM method, we have designed massive experiments on the popular WS-DREAM dataset. The reported experimental results show that our TLTM method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and time cost. © 2024, The Authors. All rights reserved.

关键词： Quality of service

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

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arXiv 2022年

作者： Zhang, Songling Yang, Zhaohui Chen, Mingzhe Liu, Danpu Wong, Kai-Kit Poor, H. Vincent The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310007 China Zhejiang Lab. Hangzhou31121 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS. Copyright © 2022, The Authors. All rights reserved.

关键词： Multicasting

Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer

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arXiv 2023年

作者： Li, Zheng Zhu, Zhengyu Chu, Zheng Guan, Yingying Mi, De Liu, Fan Yang, Lie-Liang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China National Mobile Communications Research Laboratory Southeast University Nanjing210019 China University of Surrey GuildfordGU2 7XH United Kingdom School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy transfer

Performance Optimization of Energy Efficient Semantic Communications over Wireless Networks

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Performance Optimization of Energy Efficient Semantic Commun...

IEEE Conference on Vehicular Technology (VTC)

作者： Zhaohui Yang Mingzhe Chen Zhaoyang Zhang Chongwen Huang Qianqian Yang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineer Institute of Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (纸本)9781665454698

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks is investigated. In the considered model, each user first extracts the semantic information from its large-scale data, and then transmits the small-sized semantic information to the base station (BS) which recovers the original data. Due to the limited energy budget of wireless users, both local computational energy and transmission energy must be considered. This joint computation and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the network under a latency constraint. To solve this problem, an iterative algorithm is proposed where the optimal solution for joint bandwidth allocation, power control, and computation frequency optimization problem can be obtained. Numerical results show the effectiveness of the proposed algorithm.

关键词： Wireless networks Computational modeling Semantics Information retrieval Energy efficiency Data models Resource management

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

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arXiv 2022年

作者： Ahmed, Fawad Rehman, Muneeb Ur Ahmad, Jawad Khan, Muhammad Shahbaz Boulila, Wadii Srivastava, Gautam Lin, Jerry Chun-Wei Buchanan, William J. Department of Cyber Security Pakistan Navy Engineering College NUST Karachi75350 Pakistan Department of Electrical Engineering HITEC University Taxila Pakistan School of Computing Edinburgh Napier University Edinburgh United Kingdom Department of Electrical Engineering HITEC University Taxila47080 Pakistan Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory University of Manouba Manouba Tunisia Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept of Computer Science and Math Lebanese American University Beirut Lebanon Western Norway University of Applied Sciences Bergen Norway

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 toP × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption. © 2022, CC BY.

关键词： DNA

SpEx+: A complete time domain speaker extraction network 21

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SpEx+: A complete time domain speaker extraction network

21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

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Procedia computer science 2021年 194卷 45-59页

作者： Muhammad Haleem Junejo Ab Al-Hadi Ab Rahman Riaz Ahmed Shaikh Kamaludin Mohamad Yusof Dileep Kumar Imran Memon Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science & Engineering Zhejiang University Hangzhou china Department of computer science bahria university Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET.

关键词： VANET Trust Model Machine Learning