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Optimization Design for Federated Learning in Heterogeneous 6G Networks

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arXiv 2023年

作者： Luo, Bing Ouyang, Xiaomin Sun, Peng Han, Pengchao Ding, Ningning Huang, Jianwei The Data Science Research Center the Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan China The department of Information Engineering The Chinese University of Hong Kong Hong Kong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Life Test Plan Optimization for Electrical Insulation During Temperature Load: A Semi-Optimal Approach

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Life Test Plan Optimization for Electrical Insulation During...

International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Vishnoi Sanjeev Kumar Verma Vishal Sharma K. Yuvaraj S. Gopinath Deepika Dongre Department of ECE TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Film & Television Studies School of Media IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore Vishwakarma Institute of Information Technology Pune

Considering that the Arrhenius equation between stress condition and lifetime is valid and that the proportional lifespan at an excessive pressure reflects some reliable statistical properties. It is essential to demonstrate the highest diagnostic strategy from the perspective of effectiveness under such living scenarios. Knowing the semi-optimal test plan, where effectiveness is similar to that of the maximum one and test conditions are straightforward, is additionally helpful. The goal of minimization is to determine the ideal amount of test subjects with each test stressors, and we assume that there are three sample stress levels. The root-average-squared error for the duration in use circumstance serves as the benchmark for optimization problems. We were using the hyperparameters in an actual trial situation to adjust for realism. If predictability of the Characteristic curve is necessary, we have ended up finding that there is just a subtle distinction between both the optimal results obtained and the mainstream test result when making comparisons it to the data achieved that use the mainstream tester, in which test samples are equitably represented to each experiment anxiety levels. The traditional testing phase is one of the partially ideal test plans, in our opinion. The agreement between the theory and computer findings has been examined.

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Bioelectronic protein nanowire sensors for ammonia detection

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Nano Research 2020年第5期13卷 1479-1484页

作者： Alexander F.Smith Xiaomeng Liu Trevor L.Woodard Tianda Fu Todd Emrick Juan M.Jiménez Derek R.Lovley Jun Yao Department of Biomedical Engineering University of MassachusettsAmherstMA 01003USA Department of Electrical and Computer Engineering University of MassachusettsAmherstMA 01003USA Department of Microbiology University of MassachusettsAmherstMA 01003USA Department of Polymer Science and Engineering University of MassachusettsAmherstMA 01003USA Department of Mechanical and Industrial Engineering University of MassachusettsAmherstMA 01003USA Institute for Applied Life Sciences(IALS) University of MassachusettsAmherstMA 01003USA

Electronic sensors based on biomaterials can lead to novel green technologies that are low cost,renewable,and *** we demonstrate bioelectronic ammonia sensors made from protein nanowires harvested from the microorganism Geobacter *** nanowire sensor responds to a broad range of ammonia concentrations(10 to 10^6 ppb),which covers the range relevant for industrial,environmental,and biomedical *** sensor also demonstrates high selectivity to ammonia compared to moisture and other common gases found in human *** results provide a proof-of-concept demonstration for developing protein nanowire based gas sensors for applications in industry,agriculture,environmental monitoring,and healthcare.

关键词： nanowire protein nanowire biomaterial bioelectronics biosensor ammonia sensor

RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

Dual-VQE: A quantum algorithm to lower bound the ground-state energy

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arXiv 2023年

作者： Westerheim, Hanna Chen, Jingxuan Holmes, Zoë Luo, Ivy Nuradha, Theshani Patel, Dhrumil Rethinasamy, Soorya Wang, Kathie Wilde, Mark M. School of Applied and Engineering Physics Cornell University IthacaNY14850 United States School of Electrical and Computer Engineering Cornell University IthacaNY14850 United States Department of Computer Science Cornell University IthacaNY14850 United States LausanneCH-1015 Switzerland

The variational quantum eigensolver (VQE) is a hybrid quantum–classical variational algorithm that produces an upper-bound estimate of the ground-state energy of a Hamiltonian. As quantum computers become more powerful and go beyond the reach of classical brute-force simulation, it is important to assess the quality of solutions produced by them. Here we propose a dual variational quantum eigensolver (dual-VQE) that produces a lower-bound estimate of the ground-state energy. As such, VQE and dual-VQE can serve as quality checks on their solutions;in the ideal case, the VQE upper bound and the dual-VQE lower bound form an interval containing the true optimal value of the ground-state energy. The idea behind dual-VQE is to employ semi-definite programming duality to rewrite the ground-state optimization problem as a constrained maximization problem, which itself can be bounded from below by an unconstrained optimization problem to be solved by a variational quantum algorithm. When using a convex combination ansatz in conjunction with a classical generative model, the quantum computational resources needed to evaluate the objective function of dual-VQE are no greater than those needed for that of VQE. We simulated the performance of dual-VQE on the transverse-field Ising model, and found that, for the example considered, while dual-VQE training is slower and noisier than VQE, it approaches the true value with error of order 10−2 Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum computers

A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation

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A Robust Detection Framework for Intelligent Growth Monitori...

Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Danhua Liu Boheng Li Sicheng You Qingyang Wu Jiaqi Wang Zhiyan Ren Yongying Liu Jianghan Tao Zhijie Zhang Shan Sun Zhiben Yin Chenyu Zhang Feng Xue Yijia Chen Graduate School of Business and Finance Waseda University Tokyo Japan Department of Applied Informatics Hosei University Tokyo Japan Faculty of Data Science City University of Macau Macau China Department of Environmental Health Sciences University of California Los Angeles CA US Graduate School of Frontier Sciences The University of Tokyo Kashiwa Japan Graduate School of Global Environmental Studies Sophia University Tokyo Japan Department of Computer Science and Technology Xinjiang College of Science & Technology Xinjiang China Graduate School of Information Production and Systems Waseda University Kitakyushu Japan Department of Math and Applied Mathematics China University of Petroleum-Beijing Karamay China Graduate School of Frontier Science The University of Tokyo Kashiwa Japan

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. This study introduces ROSE-MAMBA-YOLO, a hybrid detection framework that combines the efficiency of YOLOv11 with Mamba-inspired state-space modeling to enhance feature extraction, multi-scale fusion, and contextual representation. The model achieves a mAP@50 of 87.5%, precision of 90.4%, and recall of 83.1%, surpassing state-of-the-art object detection models. This framework offers a practical approach for precision floriculture and sets the stage for integrating advanced detection technologies into real-time crop monitoring systems, advancing intelligent, data-driven agriculture.

关键词： Deep learning Accuracy Green products Object detection Self-supervised learning Feature extraction Real-time systems Data models Monitoring Context modeling

Training Differentially Private Models with Secure Multiparty Computation

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arXiv 2022年

作者： Pentyala, Sikha Railsback, Davis Maia, Ricardo J.M. Melanson, David Dowsley, Rafael Nascimento, Anderson De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Computer Science University of Brasilia Brazil Department of Software Systems and Cybersecurity Monash University Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Mila - Quebec Ai Institute Canada

We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach, while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

PARTITIONS AND ELEMENTARY SYMMETRIC POLYNOMIALS - AN EXPERIMENTAL APPROACH

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arXiv 2024年

作者： Ballantine, Cristina Beck, George Merca, Mircea Department of Mathematics and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Mathematics and Statistics Dalhousie University HalifaxNSB3H 4R2 Canada Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center University Politehnica of Bucharest BucharestRO-060042 Romania

Given a partition λ, we write ej(λ) for the jth elementary symmetric polynomial ej evaluated at the parts of λ and ejpA(n) for the sum of ej(λ) as λ ranges over the set of partitions of n with parts in A. For ejpA(n), we prove analogs of the classical formula for the partition function, (formula presented), where σ1 is the sum of divisors function. We prove several congruences for e2p4(n), the sum of e2 over the set of partitions of n into four parts. Define the function prej(λ) to be the multiset of monomials in ej(λ), which is itself a partition. If A is a set of partitions, we define prej(A) to be the set of partitions prej(λ) as λ ranges over A. If P(n) is the set of all partitions of n, we conjecture that the number of odd partitions in pre2(P(n)) is at least the number of distinct partitions. We prove some results about pre2(B(n)), where B(n) is the set of binary partitions of n. We conclude with conjectures on the log-concavity of functions related to ejp(n), the sum of ej(λ) for all λ ∈ P(n).MSC Codes 05E05, 11P81 © 2024, CC BY.

关键词： Polynomials

Weighted dynamic computer generated holography for 3D image display with a commercial video projector

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Weighted dynamic computer generated holography for 3D image ...

Digital Holography and Three-Dimensional Imaging, DH 2021 - Part of OSA Imaging and applied Optics Congress 2021

作者： Xu, Jiayi Curtis, Vincent R. Caira, Nicholas W. Pégard, Nicolas C. Department of Computer Science University of North Carolina Chapel HillNC United States Department of Applied Physical Sciences University of North Carolina Chapel HillNC United States Department of Biomedical Engineering University of North Carolina Chapel HillNC United States

We have developed a new algorithm to generate 3D holograms with a MEMSbased display device and a laser light source. Our technique rapidly superimposes mutually optimized coherent frames by encoding them into 8-bit RG... 详细信息

ISBN: (纸本)9781557528209

关键词： computer generated holography