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31st Annual IEEE International Conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Zhang, Bo Davis, Philip E. Zhang, Zhao Teranishi, Keita Parashar, Manish Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ08854 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9798331509095

In-situ workflows have emerged as an attractive approach for addressing data movement challenges at very large scales. Since GPU-based architectures dominate the HPC landscapes, porting these in-situ workflows, and, specifically, the inter-application data exchange, to GPU-based systems can be challenging. Technologies such as GPUDirect RDMA (GDR), which is typically used for I/O in GPU applications as an optimization that circumvents the CPU overhead, can be leveraged to support bulk data exchanges between GPU applications. However, current GDR design often lacks performance portability across HPC clusters built with different hardware configurations. Furthermore, the local CPU may also be effectively used as an auxiliary communication mechanism to offload data exchanges. In this paper, we present a dual channel dual staging approach for efficient, scalable, and performance-portable inter-application data exchange for in-situ workflows. This approach exploits the data access pattern within in-situ workflows along with the inherent execution asynchrony to accelerate data exchanges and, at the same time, improve performance portability. Specifically, the dual channel dual staging method leverages both the local CPU and the remote data staging server to build a hierarchical joint staging area and uses this staging area to transform blocking inter-application bulk data exchanges into best-effort local data movements between GPU and CPU. The dual channel dual staging is implemented as a portability extension of the Dataspaces-GPU staging framework. We present an experimental evaluation of its performance, portability, and scalability using this implementation on three leadership GPU clusters. The evaluation results demonstrate that the dual channel dual staging method saves up to 75% in data-exchange time compared to host-based, GDR, and alternate portable designs, while maintaining scalability (up to 512 GPUs) and performance portability across the three platforms. © 2

关键词： Information management

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Sensing Safety Analysis for Vehicular Networks with Integrated Sensing and Communication (ISAC)

arXiv

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arXiv 2025年

作者： Shui, Tingyu Saad, Walid Chen, Mingzhe Bradley Department of Electrical and Computer Engineering Virginia Tech AlexandriaVA22305 United States Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

Integrated sensing and communication (ISAC) emerged as a key feature of next-generation 6G wireless systems, allowing them to achieve high data rates and sensing accuracy. While prior research has primarily focused on addressing communication safety in ISAC systems, the equally critical issue of sensing safety remains largely ignored. In this paper, a novel threat to the sensing safety of ISAC vehicle networks is studied, whereby a malicious reconfigurable intelligent surface (RIS) is deployed to compromise the sensing functionality of a roadside unit (RSU). Specifically, a malicious attacker dynamically adjusts the phase shifts of an RIS to spoof the sensing outcomes of a vehicular user (VU)’s echo delay, Doppler shift, and angle-of-departure (AoD). To achieve spoofing on Doppler shift estimation, a time-varying phase shift design on the RIS is proposed. Furthermore, the feasible spoofing frequency set with respect to the Doppler shift is analytical derived. Analytical results also demonstrate that the maximum likelihood estimator (MLE) of the AoD can be significantly misled under spoofed Doppler shift estimation. Simulation results validate our theoretical findings, showing that the RIS can induce a spoofed velocity estimation from 0.1 m/s to 14.9 m/s for a VU with velocity of 10 m/s, and can cause an AoD estimation error of up to 65◦ with only a 5◦ beam misalignment. Copyright © 2025, The Authors. All rights reserved.

关键词： Maximum likelihood

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When Zero-Trust Meets Federated Learning

When Zero-Trust Meets Federated Learning

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xinran Wang, Dan Zhu, Yifei Chen, Weilong Chang, Zheng Han, Zhu The Hong Kong Polytechnic University Department of Computing Hong Kong Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model's robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients' trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system's betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model's robustness to betrayal. © 2024 IEEE.

关键词： Federated learning

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A Joint Gradient and Loss Based Clustered Federated Learning Design

A Joint Gradient and Loss Based Clustered Federated Learning...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lin, Licheng Yang, Zhaohui Wu, Yusen Liu, Yuchen Chen, Mingzhe Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798350351255

In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training. First, the server has limited FL training information (i.e., the parameter server can only obtain the FL model information of each device) and limited computational power for finding the differences among a large amount of devices. Second, each device does not have the data information of other devices for device clustering and can only use global FL model parameters received from the server and its data information to determine its cluster identity, which will increase the difficulty of device clustering. To overcome these two challenges, we propose a joint gradient and loss based distributed clustering method in which each device determines its cluster identity considering the gradient similarity and training loss. The proposed clustering method not only considers how a local FL model of one device contributes to each cluster but also the direction of gradient descent thus improving clustering speed. By delegating clustering decisions to edge devices, each device can fully leverage its private data information to determine its own cluster identity, thereby reducing clustering overhead and improving overall clustering performance. Simulation results demonstrate that our proposed clustered FL algorithm can reduce clustering iterations by up to 99% compared to the existing baseline. © 2024 IEEE.

关键词： Federated learning

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The Utilization of a Parallel computing Algorithm for Accelerating Switching-Level Modeling of Power Electronics Simulations in a T-Type PV Inverter

The Utilization of a Parallel Computing Algorithm for Accele...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Buck F. Brown Liwei Wang Zheyu Zhang Johan Enslin Yi Li Department of Electrical and Computer Engineering Clemson University North Charleston SC USA School of Computing Clemson University North Charleston SC USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

High-fidelity, switching-level modeling of power electronics simulations can be computationally intensive and time-consuming. This computational burden has recently escalated further due to the increased number of converters implemented in a system, e.g., renewable energy systems. Unlike switching modeling, average modeling can alleviate the computational burden and adequately represent a converter’s behavior for controller design. However, average modeling does not encapsulate switching harmonics, which are crucial for power electronics design, operation, and reliability studies. This paper proposes a novel way to accelerate the switching-level modeling of simulations by leveraging parallel computing techniques and showcases this technique in a specific case study. This new technique is called the average-to-switching (A2S) model. First, the basic concept of the A2S model is outlined. Second, a case study is designed for a two-stage, T-type PV inverter. Third, electrical and thermal waveforms produced by the case study are given and discussed. The results of testing the A2S model showcase a speedup of greater than 22 times the simulation benchmark while maintaining an R 2 value of greater than 0.99.

关键词： Renewable energy sources Electric potential Accuracy Computational modeling Switches Parallel processing Power system harmonics Reliability engineering Multilevel inverters Harmonic analysis

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computing Augustin Information via Hybrid Geodesically Convex Optimization

Computing Augustin Information via Hybrid Geodesically Conve...

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IEEE International Symposium on Information Theory

作者： Guan-Ren Wang Chung-En Tsai Hao-Chung Cheng Yen-Huan Li Graduate Institute of Networking and Multimedia National Taiwan University Department of Computer Science and Information Engineering National Taiwan University Department of Electrical Engineering and Graduate Institute of Communication Enaineering National Taiwan University Department of Mathematics National Taiwan University Hon Hai (Foxconn) Quantum Computing Centre Center for Quantum Science and Engineering National Taiwan University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We propose a Riemannian gradient descent with the Poincaré metric to compute the $\mathbf{order}-\alpha$ Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of $\mathcal{O}(1/T)$ . As far as we know, this is the first algorithm with a non-asymptotic optimization error guarantee for all positive orders. Numerical experimental results demonstrate the empirical efficiency of the algorithm. Our result is based on a novel hybrid analysis of Riemannian gradient descent for functions that are geodesically convex in a Riemannian metric and geodesically smooth in another.

关键词： Measurement quantum computing Finance Linear programming Convex functions Optimization Ellipsoids

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Enhancing Early Diagnosis of Autism Spectrum Disorder Using a Bayesian-Optimized Random Forest Algorithm

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Procedia computer Science 2025年 258卷 1576-1585页

作者： Tina Babu Rekha R Nair Kishore S Sheila Mahapatra Department of Computer Science and Engineering School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte University (Deemed to be University) Nitte Institute of Professional Education Mangalore India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by social communication difficulties, re- stricted interests, and repetitive behaviors. Early and accurate diagnosis is critical for timely intervention and improved outcomes. Traditional diagnostic methods, which rely on clinical observations and assessments, can be subjective and time-consuming. This study introduces a novel approach to predicting ASD using a machine learning technique, specifically a Bayesian-optimized Ran- dom Forest classification algorithm. The proposed method uses a comprehensive dataset containing various behavioral, cognitive, and demographic features associated with ASD. The Optimized Random Forest with Bayesian approach is used to categorize indi- viduals into two groups: those who have ASD and those who do not. To develop this ensemble learning technique, multiple decision trees are used, and to achieve higher performance as well as better capability to generalize, the hyperparameters are optimized with Bayesian optimization. Preliminary analysis and comparison with existing databases substantiated the findings and revealed the effectiveness of the proposed approach. The Bayesian-optimized Random Forest classifier achieved an accuracy value of 0.9890. Such performance is highly promising and could enable increased use of machine learning methods in timely and accurate diagno- sis of ASD. The proposed approach provides recommendations for a verified diagnostic technique when implemented and creates a strong foundation for future investigations into the use of machine learning for identifying and supporting children with ASD.

关键词： Autism Spectrum Disorder (ASD) Machine Learning Bayesian Optimization Random Forest Early Diagnosis Predictive Modeling

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Tailored Beam-Forming for Atomic Systems with Integrated Optics

Tailored Beam-Forming for Atomic Systems with Integrated Opt...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Gillenhaal J. Beck Karan K. Mehta Jonathan P. Home Institute for Quantum Electronics ETH Zürich Zürich Switzerland Electrical and Computer Engineering Cornell University Ithaca NY USA

We demonstrate micron-scale focusing of fundamental and higher-order modes, including Laguerre-Gaussian “vortex” beams, from waveguide-to-free-space grating outcouplers at ultraviolet and visible wavelengths.

ISBN: (纸本)9798350369311

关键词： Integrated optics Atom optics Atomic beams Focusing Waveguide lasers Lasers and electrooptics Laser modes Laser beams Gratings Electro-optical waveguides

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Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning

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Energy and AI 2024年第4期18卷 459-469页

作者： Daniel C.May Matthew Taylor Petr Musilek Electrical and Computer Engineering University of AlbertaEdmontonT6G 2R3ABCanada Computing Science University of Alberta&Alberta Machine Intelligence InstituteEdmontonT6G 2R3ABCanada Applied Cybernetics University of Hradec Králové50003Hradec KrálovéCzech Republic

As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity *** significant aspect of this issue is the notable increase in net load variability at the grid *** energy,implemented through local energy markets,has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized,indirect demand response on a community ***-free control approaches,such as deep reinforcement learning(DRL),show promise for the decentralized automation of participation within this *** studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics,overlooking the crucial goal of reducing community-level net load *** study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven,autonomous local energy market(ALEX).In this setting,agents do not share information and only prioritize individual bill *** study unveils a clear correlation between bill reduction and reduced net load *** impact on net load variability is assessed over various time horizons using metrics such as ramping rate,daily and monthly load factor,as well as daily average and total peak export and import on an open-source *** examine the performance of the proposed DRL method,its agents are benchmarked against a optimal near-dynamic programming method,using a no-control scenario as the *** dynamic programming benchmark reduces average daily import,export,and peak demand by 22.05%,83.92%,and 24.09%,*** RL agents demonstrate comparable or superior performance,with improvements of 21.93%,84.46%,and 27.02%on these *** demonstrates that DRL can be effectively employed for such tasks,as they are inherent

关键词： Reinforcement learning Deep reinforcement learning Distributed energy resources Local energy markets Demand response Distributed energy resource management Transactive energy

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2025年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Institute for Data Science and Computing University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada Shenzhen Research Institute of Big Data Future Network of Intelligence Institute The Chinese University of Hon g Kong School of Science and Engineering Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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