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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Hongyi Yu, Han Tang, Xiaoli Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average. Copyright 2024 by the author(s)

关键词： Federated learning

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Lightator: An Optical Near-Sensor Accelerator with Compressive Acquisition Enabling Versatile Image Processing 24

Lightator: An Optical Near-Sensor Accelerator with Compressi...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Morsali, Mehrdad Reidy, Brendan Najafi, Deniz Tabrizchi, Sepehr Imani, Mohsen Nikdast, Mahdi Roohi, Arman Zand, Ramtin Angizi, Shaahin Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States School of Computing University of Nebraska-Lincoln LincolnNE United States Department of Computer Science University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO United States

ISBN: (纸本)9798400706011

This paper proposes a high-performance and energy-efficient optical near-sensor accelerator for vision applications, called Lightator. Harnessing the promising efficiency offered by photonic devices, Lightator features innovative compressive acquisition of input frames and fine-grained convolution operations for low-power and versatile image processing at the edge for the first time. This will substantially diminish the energy consumption and latency of conversion, transmission, and processing within the established cloud-centric architecture as well as recently designed edge accelerators. Our device-to-architecture simulation results show that with favorable accuracy, Lightator achieves 84.4 Kilo FPS/W and reduces power consumption by a factor of ∼24× and 73× on average compared with existing photonic accelerators and GPU baseline. © 2024 Copyright held by the owner/author(s).

关键词： Photonic devices

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images 14

Generative Adversarial Networks for Spatio-Spectral Compress...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begum Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Technische Universität Berlin Faculty of Electrical Engineering and Computer Science Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798331513139

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCsE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCsE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2024 IEEE.

关键词： Generative adversarial networks

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Computational Optimization of RIS-Enhanced Backscatter and Direct Communication for 6G IoT:A DDPG-Based Approach with Physical Layer Security

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computer Modeling in engineering & Sciences 2025年第3期142卷 2191-2210页

作者： Syed Zain Ul Abideen Mian Muhammad Kamal Eaman Alharbi Ashfaq Ahmad Malik Wadee Alhalabi Muhammad Shahid Anwar Liaqat Ali College of Computer Science and Technology Qingdao UniversityQingdao266071China School of Electronic Science and Engineering Southeast UniversityNanjing210018China Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of Quality Assurance Al-Kawthar UniversityKarachi75300Pakistan Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan

The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient *** study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication *** traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter *** propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase *** optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy ***,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS *** results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G *** work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.

关键词： Computational optimization reconfigurable intelligent surfaces(RIS) 6G networks IoT and DDPG physical layer security(PLS) backscatter communication

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HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI 24

HiRISE: High-Resolution Image Scaling for Edge ML via In-Sen...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Reidy, Brendan Tabrizchi, Sepehr Mohammadi, Mohammadreza Angizi, Shaahin Roohi, Arman Zand, Ramtin Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States School of Computing University of Nebraska-Lincoln LincolnNE United States Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States

ISBN: (纸本)9798400706011

With the rise of tiny IoT devices powered by machine learning (ML), many researchers have directed their focus toward compressing models to fit on tiny edge devices. Recent works have achieved remarkable success in compressing ML models for object detection and image classification on microcontrollers with small memory, e.g., 512kB SRAM. However, there remain many challenges prohibiting the deployment of ML systems that require high-resolution images. Due to fundamental limits in memory capacity for tiny IoT devices, it may be physically impossible to store large images without external hardware. To this end, we propose a high-resolution image scaling system for edge ML, called HiRISE, which is equipped with selective region-of-interest (ROI) capability leveraging analog in-sensor image scaling. Our methodology not only significantly reduces the peak memory requirements, but also achieves up to 17.7× reduction in data transfer and energy consumption. © 2024 Copyright held by the owner/author(s).

关键词： Image compression

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Novel Functional Community Detection in Networked Smart Grid Systems-Based Improved Louvain Algorithm

Novel Functional Community Detection in Networked Smart Grid...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

ISBN: (纸本)9781665490719

Despite using Community Detection Algorithms (CDA) in various network partitioning real-world applications, these algorithms tend to fail to partition complex weighted functional networks such as power grids. When contingency events occur, appropriate power grid partitioning enables an effective distributed control strategy to detect system faults and cyberattacks and accelerate electrical assets recovery through fast remedial control. This paper addresses the CDA by considering the functional structure of the power grid. When comparing the topological and functional structures of the power grid network, electrical Coupling Strength (ECS) reflects both direct and indirect power flows, unlike a conventional adjacency matrix, which only considers an unweighted direct connection between buses. The proposed Improved Louvain Algorithm (ILA) employs the ECS patterns to represent the network connection. Here, the quality function is modified as electrical modularity Qe to assess the goodness of the produced clusters. Large Qe indicates that the power transmission within the partition is dense compared to power transmission with other partitions. The algorithm has been tested on IEEE 39-bus and 118-bus systems. Using different case scenarios, the simulation results demonstrate the high effectiveness of the proposed ILA to the traditional Louvain algorithm in partitioning the complex networked electrical grid into adequate subsystems. © 2023 IEEE.

关键词： Intrusion detection

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Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

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computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Accelerating Switching Model-Based Simulation Through Parallel computing

Accelerating Switching Model-Based Simulation Through Parall...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Li, Yi Zhang, Zheyu Edrington, Christopher Jin, Shuangshuang Clemson University Holocombe Department of Electrical and Computer Engineering Charleston United States Clemson University School of Computing Charleston United States

ISBN: (纸本)9798350316445

Power electronics converters become an enabler for future power and energy system. High-fidelity power electronics simulation can be compute-intensive and time-consuming because of the higher switching frequency (i.e., switching actions per second) of converter using advanced power semiconductors and an increased number of converters applied in the system, such as renewable energy with energy storage and electrified transportation. Unlike the switching model, average modeling can efficiently alleviate the computational burden, and adequately represent converters' behavior for the controller design, but sacrifice the resolution for switching ripples/harmonics, which are also crucial for power electronics design, operation, and reliability studies. This digest proposes to accelerate the switching model-based simulation by leveraging a fast but low-fidelity average model plus parallel computing technique, entitled the average-to-switching (A2S) method. First, in comparison with today's sequentially-computing approach, the basic concept of the A2S method by introducing parallel computing is presented. Then, the detailed methodology of the proposed A2S approach is described with the derivation of the algorithm suitable for paralleling computing. Finally, a case study with a widely applied two-level voltage source converter is performed and a comparison among these methods is summarized. It is observed that this proposed method performed >5 times speedup than the benchmark while maintaining an r-square value of the results > 0.99. © 2023 IEEE.

关键词： parallel computing power electronics simulation accuracy simulation time

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Fire Security Systems Analysis and Internet of Things Implications

Fire Security Systems Analysis and Internet of Things Implic...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Fuerte, Susana Rodriguez Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

This paper presents the current use of the Internet of Things (IoT) in fire evacuation and extinction. It examines the different approaches to the problem and technologies like Building Information Modeling (BIM) and mathematical algorithms that can be used to determine the optimal evacuation route. It also evaluates existing fire security solutions such as smoke, flame, motion, and gas sensors, LED lights, buzzers, and SMS modules. Entities that specialize in Residential and Commercial Security Systems and Home Automation are also discussed, along with the services they offer. The main objective of this study is to understand current systems and resources regarding fire evacuation and extinction systems and to analyze different developments in smart buildings to create an efficient system for fire detection and evacuation. © 2023 IEEE.

关键词： Internet of things

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