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LDPC Coded Compressive Sensing for Joint Source-Channel Coding in Wireless Sensor Networks

IEEE Transactions on Vehicular Technology 2023年第2期72卷 2145-2160页

作者： Chen, Jue Shao, Shuai Wang, Tsang-Yi Wu, Jwo-Yuh Li, Chih-Peng Ng, Soon Xin Maunder, Robert G. Hanzo, Lajos University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan

The novel concept of joint Compressive Sensing (CS) and Low Density Parity Check (LDPC) coding is conceived for Joint Source-Channel Coding (JSCC) in Wireless Sensor Networks (WSNs) supporting a massive number of signals. More explicitly, we demonstrate this concept for a specific scheme, which supports a massive number of signals simultaneously, using a small number of Internet of Things Nodes (IoTNs) based on the concept of CS. The compressed signals are LDPC coded in order to protect them from poor transmission channels. We also propose the new iterative joint source-channel decoding philosophy for exchanging soft extrinsic information, which combines CS decoding and LDPC decoding by merging their respective factor graphs. We then characterize this scheme using Extrinsic Information Transfer (EXIT) chart analysis. Our BLock Error Rate (BLER) results show that the proposed iterative joint LDPC-CS decoding scheme attains about 1.5 dB gain at a BLER of 103 compared to a benchmarker, which employs separate CS and LDPC decoding. Naturally, this gain is achieved at the cost of approximately doubling the complexity of the proposed iterative joint LDPC-CS decoding scheme. © 1967-2012 IEEE.

关键词： Wireless sensor networks

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Privacy Protection for Federated Learning in OFDM Aided Over-the-Air Computation System

Privacy Protection for Federated Learning in OFDM Aided Over...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wang, Yujuan Fan, Rongfei Hu, Han Zhang, Ning Beijing Institute of Technology The School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology The School of Information and Electronics Beijing100081 China University of Windsor The Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

ISBN: (纸本)9798350345384

Federated learning (FL) an emerging distributed and privacy-protecting machine learning paradigm. In FL, training is completed through iterative local training and gradient aggregation among multiple mobile devices (MDs) and the edge server (ES). Over-the-air computation can help to achieve fast aggregation when multiple MDs need to offload their local gradients to the ES. Orthogonal frequency division modulation (OFDM) can further speed up aggregation through enabling simultaneous symbol transmission over multiple fading blocks. In this paper, we focus on the FL system with OFDM aided over-the-air computation technique implemented. To overcome the challenge of non-linear distortion at the amplifier of OFDM transmitter and protect the privacy of each individual MU, we propose a new aggregation method, which normalizes the local gradient first and then add uniformly distributed artificial noise, before it is offloaded to the ES. With our proposed aggregation method, the performance of differential privacy (DP) is analyzed, which disclose the quantization relationship between the variance of artificial noise and the level of privacy protection. We also analyze the peak power at OFDM transmitter by our proposed method, which is shown to be surely less than traditional method. Numerical results verifies the convergence and effectiveness of our proposed strategy. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Model Extraction Attacks on Split Federated Learning

arXiv

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arXiv 2023年

作者： Li, Jingtao Rakin, Adnan Siraj Chen, Xing Yang, Li He, Zhezhi Fan, Deliang Chakrabarti, Chaitali School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients’ data but turns out to be highly vulnerable to Intellectual Property (IP) threats. Since FL periodically collects and distributes the model parameters, a free-rider can download the latest model and thus steal model IP. Split Federated Learning (SFL), a recent variant of FL that supports training with resource-constrained clients, splits the model into two, giving one part of the model to clients (client-side model), and the remaining part to the server (server-side model). Thus SFL prevents model leakage by design. Moreover, by blocking prediction queries, it can be made resistant to advanced IP threats such as traditional Model Extraction (ME) attacks. While SFL is better than FL in terms of providing IP protection, it is still vulnerable. In this paper, we expose the vulnerability of SFL and show how malicious clients can launch ME attacks by querying the gradient information from the server side. We propose five variants of ME attack which differs in the gradient usage as well as in the data assumptions. We show that under practical cases, the proposed ME attacks work exceptionally well for SFL. For instance, when the server-side model has five layers, our proposed ME attack can achieve over 90% accuracy with less than 2% accuracy degradation with VGG-11 on CIFAR-10. © 2023, CC BY.

关键词： Extraction

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Automated Detection and Prediction of Hospital-Acquired Infections Using IoT and Linear Regression

Automated Detection and Prediction of Hospital-Acquired Infe...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Cheepurupalli Raghuram Subramani Roychoudri C. Viswanathan C. Chandru Vignesh Geetha Ponnaian B. Meenakshi Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering Usha Rama College of Engineering and Technology Vijayawada Andhra Pradesh India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tami Nadu India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tami Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Major source of increased healthcare settings morbidity, and death, hospital-acquired infections (HAIs) need urgent attention in healthcare facilities. Conventional approaches to HAIs detection and prevention mostly on retrospective analysis and manual surveillance, which can cause intervention delays and less-than-ideal results. In this research, it provides a new way to deal with these issues by automatically detecting and predicting HAIs using the power of the Internet of Things (IoT) and linear regression (LR) methods. Using IoT sensors throughout health care facilities to continually track vital signs, patient interactions, and healthcare procedures. Advanced statistical approaches, such as LR models, are used to the real-time data streams generated by these sensors to detect patterns and connections that may indicate the existence of HAIs and the dynamics of their transmission. The proposed method allows for the early detection of possible infection clusters and high-risk regions in the hospital by combining several data sources, such as patient demographics, medical history, and clinical processes. Predictive analytics to plan for the possibility of HAIs outbreaks and determine which preventative measures will be most effective. Healthcare institutions may improve their infection control policies, allocate resources more efficiently, and decrease the occurrence of automated systems. It shows that this strategy works, which bodes well for its future use in hospital settings where patient safety is paramount, and HAIs control is under constant scrutiny.

关键词： Accuracy Hospitals System performance Surveillance Linear regression Predictive models Data models Safety Internet of Things Streams

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Integral Reinforcement Learning Control for a Class of Unknown Nonlinear Systems with an Application to a Microgrid System

Integral Reinforcement Learning Control for a Class of Unkno...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Shanyong Xu Hanguang Su Xiaodong Liang Jinzhu Yang Jiawei Wang Xinyang Luan College of Information Science and Engineering Northeastern University Shenyang China Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada School of Computer Science and Engineering Northeastern University Shenyang China Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle United Kingdom

In this paper, a brand-new technique based on integral reinforcement learning (IRL) combined with the event-triggered control (ETC) for multiplayer non-zero-sum (NZS) game is proposed, taking into account nonlinear systems with uncertain system drift dynamics. System drift dynamics are no longer necessary for controller design with the IRL method. Furthermore, this method is implemented online, in contrast to other iterative calculating techniques. In this instance, the NZS game problems can be resolved by combining the IRL algorithm and the event-triggered control architecture. It offers a new triggering condition and lessens the computational and communication overhead of the entire control process. The system’s stability is ensured at the same time. An example is then given to show how well our method works.

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Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames

Manipulation-Oriented Object Perception in Clutter through A...

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IEEE-RAS International Conference on Humanoid Robots

作者： Xiaotong Chen Kaizhi Zheng Zhen Zeng Cameron Kisailus Shreshtha Basu James Cooney Jana Pavlasek Odest Chadwicke Jenkins Department of Electrical Engineering and Computer Science Robotics Institute at the University of Michigan Ann Arbor MI USA

ISBN: (纸本)9798350309805

In order to enable robust operation in unstructured environments, robots should be able to generalize ma-nipulation actions to novel object instances. For example, to pour and serve a drink, a robot should be able to recognize novel containers which afford the task. Most importantly, robots should be able to manipulate these novel containers to fulfill the task. To achieve this, we aim to provide robust and generalized perception of object affordances and their associated manipulation poses for reliable manipulation. In this work, we combine the notions of affordance and category-level pose, and introduce the Affordance Coordinate Frame (ACF). With ACF, we represent each object class in terms of individual affordance parts and the compatibility between them, where each part is associated with a part category-level pose for robot manipulation. In our experiments, we demonstrate that ACF outperforms state-of-the-art methods for object detection, as well as category-level pose estimation for object parts. We further demonstrate the applicability of ACF to robot manipulation tasks through experiments in both simulation and real world environment.

关键词： Deep learning Affordances Robot kinematics Pose estimation Humanoid robots Object detection Containers

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Efficient Numerical Analysis for Few-Atom Cavity Quantum Electrodynamics Using the Coupling Matrix Approach

Efficient Numerical Analysis for Few-Atom Cavity Quantum Ele...

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Antennas and Propagation Society International Symposium

作者： Christopher J. Ryu Dong-Yeop Na Weng C. Chew Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL USA Department of Electrical Engineering Pohang University of Science and Technology Pohang Republic of Korea Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Numerical analysis of few-atom cavity quantum electrodynamics (QED) is challenging due to the exponentially scaling size of the Hilbert space when multiple quantized electromagnetic modes are considered. To overcome this, we propose an approach based on coupling matrix transformations that allows adjustment of the coupling structures of QED Hamiltonians so that they are more compatible with tensor network algorithms such as matrix product states. We present the relevant Hamiltonians along with the coupling matrix transformation scheme and present few-atom cavity QED simulations in the time domain that is able to characterize the interactions between atoms efficiently.

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Analyzing pre-commitment strategies in General Lotto games

Analyzing pre-commitment strategies in General Lotto games

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American Control Conference (ACC)

作者： Keith Paarporn Rahul Chandan Dan Kovenock Mahnoosh Alizadeh Jason R. Marden Department of Computer Science University of Colorado Colorado Springs Department of Electrical and Computer Engineering University of California Santa Barbara Economic Science Institute Argyros School of Business and Economics Chapman University

A salient feature of many optimal decision-making policies in adversarial environments is a level of unpredictability, or randomness, which keeps opponents uncertain about the system’s strategies. These considerations, along with feedback from adversarial behaviors, are crucial in ensuring the security of modern infrastructures and complex systems. This paper considers policies that do just the opposite, namely ones that reveal strategic intentions to an opponent before engaging in competition. We consider such scenarios in the context of General Lotto games, which models the competitive allocation of resources between opposing players. Here, we consider a dynamic extension where one of the players has the option to publicly pre-commit assets to a battlefield in the first stage. In response, the opponent decides whether to secure the battlefield by matching the pre-commitment with its own resources, or to withdraw from it entirely. They then engage over the remaining set of battlefields in the second stage. We show that the weaker-resource player can have incentives to pre-commit when the battlefield values are asymmetric across players. Previous work asserts this never holds when the values are symmetric across players. Our analysis demonstrates the viability of alternate strategic mechanisms that a competitor may be able to employ.

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Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models

arXiv

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arXiv 2024年

作者： Fang, Zihan Lin, Zheng Chen, Zhe Chen, Xianhao Gao, Yue Fang, Yuguang School of Computer Science Fudan University Shanghai200438 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong

Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). For many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, combined with high computational and communication demands, makes it hard to apply to handle downstream tasks. More importantly, private edge servers often possess varying computing and network resources in real-world scenarios, introducing additional complexities to LLM fine-tuning. To tackle these problems, we design and implement an automated federated pipeline, named FedPipe, to fine-tune LLMs on heterogeneous edge servers with minimal training cost and no additional inference latency. FedPipe firstly identifies the weights to be fine-tuned based on their contributions to the LLM training. It then configures a low-rank adapter for each selected weight within the resource constraints of the edge server and aggregates these local adapters from all edge servers to fine-tune the whole LLM. Finally, it appropriately quantizes the parameters of LLM to reduce memory space according to the requirements of edge servers. Extensive experiments demonstrate that FedPipe expedites model training and achieves higher accuracy than the state-of-the-art benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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HyperColorization: Propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images

arXiv

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arXiv 2024年

作者： Aydin, M. Kerem Guo, Qi Alexander, Emma Department of Computer Science McCormick School of Engineering and Applied Science Northwestern University 2233 Tech Drive EvanstonIL60208 United States Elmore Family School of Electrical and Computer Engineering Purdue University 501 Northwestern Avenue West LafayetteIN47907 United States

Hyperspectral cameras face challenging spatial-spectral resolution trade-offs and are more affected by shot noise than RGB photos taken over the same total exposure time. Here, we present a colorization algorithm to reconstruct hyperspectral images from a grayscale guide image and spatially sparse spectral clues. We demonstrate that our algorithm generalizes to varying spectral dimensions for hyperspectral images, and show that colorizing in a low-rank space reduces compute time and the impact of shot noise. To enhance robustness, we incorporate guided sampling, edge-aware filtering, and dimensionality estimation techniques. Our method surpasses previous algorithms in various performance metrics, including SSIM, PSNR, GFC, and EMD, which we analyze as metrics for characterizing hyperspectral image quality. Collectively, these findings provide a promising avenue for overcoming the time-space-wavelength resolution trade-off by reconstructing a dense hyperspectral image from samples obtained by whisk or push broom scanners, as well as hybrid spatial-spectral computational imaging systems. © 2024, CC BY-NC-SA.

关键词： Shot noise

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