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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints

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Joint Sparsification and Quantization for Wireless Federated...

IEEE Workshop on Signal processing Advances in Wireless Communications (SPAWC)

作者： Junshen Su Xijun Wang Xiang Chen Ray-Guang Cheng School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10.

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The Novel Sensor Network Structure for Classification processing Based on the Machine Learning Method of the ACGAN

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Sensors (Switzerland) 2019年第14期19卷 3145.-3145.页

作者： Chen, Yuantao Tao, Jiajun Wang, Jin Chen, Xi Yang, Kai School of Computer and Communication Engineering & Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportatio Changsha University of Science and Technology Changsha410114 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Hunan Institute of Scientific and Technical Information Changsha410001 China Electronics & Information School Yangtze University Jingzhou434023 China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha410005 China

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more ekective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions. © Sensors (Switzerland) 2019. All Rights Reserved.

关键词： Generative adversarial networks

PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model

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arXiv 2024年

作者： Wang, Yuqing Huang, Zhongling Yang, Shuxin Tang, Hao Qiu, Xiaolan Han, Junwei Zhang, Dingwen Brain and Artificial Intelligence Laboratory School of Automation Northwestern Polytechnical University Xi’an710072 China the National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China the National Key Laboratory of Microwave Imaging Technology Chinese Academy of Sciences Beijing100090 China the Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

PolSAR data presents unique challenges due to its rich and complex characteristics. Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used. However, these formats often face issues related to usability, interpretability, and data integrity. While most feature extraction networks for PolSAR attempt to address these issues, they are typically small in size, which limits their ability to effectively extract features. To overcome these limitations, we introduce the large, powerful Segment Anything Model (SAM), which excels in feature extraction and prompt-based segmentation. However, SAM’s application to PolSAR is hindered by modality differences and limited integration of domain-specific knowledge. To address these challenges, we propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), which incorporates physical scattering characteristics and a novel prompt generation strategy to enhance segmentation performance with high data efficiency. Our approach includes a new data processing pipeline that utilizes polarimetric decomposition and semantic correlations to generate Microwave Vision Data (MVD) products, which are lightweight, physically interpretable, and information-dense. We extend the basic SAM architecture with two key contributions: The Feature-Level Fusion Prompt (FFP) module merges visual tokens from the pseudo-colored SAR image and its associated MVD, enriching them with supplementary information. When combined with a dedicated adapter, it addresses modality incompatibility in the frozen SAM encoder. Additionally, we propose the Semantic-Level Fusion Prompt (SFP) module, a progressive mechanism that leverages semantic information within MVD to generate sparse and dense prompt embeddings, refining segmentation details. Experimental evaluation on the newly constructed PhySAR-Seg datasets shows that PolSAM outperforms existing SAM-based models (without MVD) and other multimodal fusion

关键词： Polarimeters

DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection

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arXiv 2020年

作者： Chen, Zuyao Cong, Runmin Xu, Qianqian Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 16 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Modal analysis

BINARY SEQUENCES WITH A LOW CORRELATION VIA CYCLOTOMIC FUNCTION FIELDS WITH ODD CHARACTERISTICS

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arXiv 2022年

作者： Jin, Lingfei Ma, Liming Xing, Chaoping Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China School of Mathematical Sciences University of Science and Technology of China Hefei230026 China School of Electronic Information and Electric Engineering Shanghai Jiao Tong University 200240 China

Sequences with a low correlation have very important applications in communications, cryptography, and compressed sensing. In the literature, many efforts have been made to construct good sequences with various lengths where binary sequences attracts great attention. As a result, various constructions of good binary sequences have been proposed. However, most of the known constructions made use of the multiplicative cyclic group structure of finite field Fpn for a prime p and a positive integer n. In fact, all pn + 1 rational places including the place at infinity of the rational function field over Fpn form a cyclic structure under an automorphism of order pn + 1. In this paper, we make use of this cyclic structure to provide an explicit construction of binary sequences with a low correlation of length pn +1 via cyclotomic function fields over Fpn for any odd prime p. Each family of binary sequences has size pn − 2 and its correlation is upper bounded by 4 + ⌊2 · pn/2⌋. To the best of our knowledge, this is the first construction of binary sequences with a low correlation of length pn + 1 for odd prime p. Moreover, our sequences can be constructed explicitly and have competitive parameters. © 2022, CC BY.

关键词： Binary sequences

Enhancing Online Teaching Effectiveness Through Hand-Raising Detection

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Enhancing Online Teaching Effectiveness Through Hand-Raising...

information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Zhiwei Dong Xiaopan Chen Yufeng Shi Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments.

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Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification

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arXiv 2022年

作者： Hou, Junlin Xu, Jilan Zhang, Nan Zhang, Yuejie Zhang, Xiaobo Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China Children’s Hospital of Fudan University National Children’s Medical Center Shanghai China

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifically, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%. © 2022, CC BY.

关键词： COVID-19

A semi-informative aware approach using topic model for medical search

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A semi-informative aware approach using topic model for medi...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qinmin Vivian Hu Liang He Mingyao Li Jimmy Xiangji Huang E. Mark Haacke Shanghai Key Laboratory of Multidimensional Information Processing MR Research Facility Wayne State University Detroit MI USA Department of Computer Science & Technology East China Normal University Shanghai China Information Retrieval and Knowledge Management Research Lab York University Toronto Canada

We propose a semi-informative aware approach using the topic model on query expansion problem in the biomedicine domain. the demographics and disease information is applied to semi-structure the topic model as the “known” label, compared to the traditional latent topics in topic modelling. Then, we suggest to select three terms from the top ranked documents to expand the query, based on the assumption in the pseudo relevance feedback method that the top ranked results in the first retrieval around are relevant. After that, we conduct the experiments on the TREC medical records data sets with extensive analysis and discussions. Numerically, we achieve the improvements of 7.41% on MAP, 9.29% on Bpref and 5.60% on P@10 respectively over the strong baselines.

关键词： Indexes Diseases information retrieval Numerical models Biological system modeling Educational institutions Mathematical model

No-Reference Image Quality Assessment Based on Active Reasoning Module

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No-Reference Image Quality Assessment Based on Active Reason...

IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Junwei Qi Yuhao Deng Qingchun Wang Zhen Yang Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computer Science and Technology Harbin Engineering University Harbin China Shanghai Yanding Information Technology Co. Ltd. Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

We present a no-reference image-quality - assessment algorithm based on active reasoning module. This algorithm has three modules: the feature extraction module, the active reasoning module, and the quality assessment module. The active reasoning module incorporates the generator component of the generative adversarial network and enhances its structure with the Res2Net architecture. By integrating this module into the backbone feature extraction network, we improve the receptive field of each convolutional layer, enabling the network to capture information at different scales of the image. To preserve the texture information of the image, we input the gradient map of the distorted image, the distorted image itself, and the image features generated by the generator into the quality assessment module, which has a multi-feature regression networks. This module establishes a mapping model from image-feature to image-quality scores. We conducted experimental analysis of this algorithm on three widely used public datasets, confirming the excellent performance and superiorities of the presented algorithm. The results validate the effectiveness of the designed algorithm and its ability to assess image quality accurately.

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