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Efficient Federated Learning for AIoT applications Using Knowledge Distillation

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arXiv 2021年

作者： Liu, Tian Ling, Zhiwei Xia, Jun Fu, Xin Yu, Shui Chen, Mingsong MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States School of Computer Science University of Technology Sydney Australia

As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

A novel method for driving path planning with spark

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TechRxiv

TechRxiv 2021年

作者： Li, Leixiao Lin, Hao Wan, Jianxiong Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China School of Computer Science and Engineering Tianjin University of Technology China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China College of Computer and Information Engineering Inner Mongolia Agricultural University China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry China

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability. © 2021, CC BY.

关键词： Heuristic algorithms

Image super-resolution with an enhanced group convolutional neural network

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arXiv 2022年

作者： Tian, Chunwei Yuan, Yixuan Zhang, Shichao Lin, Chia-Wen Zuo, Wangmeng Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://***/hellloxiaotian/ESRGCNN. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

Contract-based Cooperative Computation and Communication Resources Sharing in Mobile Edge Computing

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Journal of Grid Computing 2023年第1期21卷 14-14页

作者： Zheng, Yifeng Zou, Lushan Zhang, Wenjie Yang, Jingmin Yang, Liwei Lin, Ziqiong School of Computer Sciences Minnan Normal University No.36 Xianqian Street Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University No.36 Xianqian Street Fujian Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology No.1 Section 3 Zhongxiao East Road Taibei610101 Taiwan College of Information and Electrical Engineering China Agricultural University No.17 Qinghua East Road Haidian District Beijing100000 China

In this paper, we consider a Mobile Edge Computing (MEC) system with a source transmitter (ST) and multiple relay nodes (RNs). ST has sufficient computing resources, but lacks communication ability, while the situation of RN is the opposite. To tackle this resource imbalance problem, we propose a cooperative computation and communication resources sharing strategy using contract theory under complete and incomplete information scenarios. The ST offers a contract combining computation time and relay power, and the RNs forward the computational results for ST in exchange for the usage of computation capacity. For complete information scenario, we derive the optimal contract to maximize the ST’s utility and we show the existence and uniqueness of optimal contract. We further explore the properties of a feasible contract for incomplete information scenario where the channel gain and relay cost are the private information of RNs and not known by ST. We analyze the necessary and sufficient conditions for a contract to be feasible and propose a sequence optimization algorithm to obtain the optimal contract. Finally, we show the feasibility and optimality of the contract through simulation results. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Mobile edge computing

Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints

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arXiv 2022年

作者： Xing, Yinghui Zhang, Yan He, Houjun Zhang, Xiuwei Zhang, Yanning National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China Research Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Information Center of Yellow River Conservancy Commission Henan Zhengzhou China

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion still exist in their fusion results due to the insufficiency in details learning and the frequency mismatch between MS and PAN. Therefore, the improvement of spatial details at the premise of reducing spectral distortion is still a challenge. In this paper, we propose a frequency-aware fusion network (FAFNet) together with a novel high-frequency feature similarity loss to address above mentioned problems. FAFNet is mainly composed of two kinds of blocks, where the frequency aware blocks aim to extract features in the frequency domain with the help of discrete wavelet transform (DWT) layers, and the frequency fusion blocks reconstruct and transform the features from frequency domain to spatial domain with the assistance of inverse DWT (IDWT) layers. Finally, the fusion results are obtained through a convolutional block. In order to learn the correspondence, we also propose a high-frequency feature similarity loss to constrain the HF features derived from PAN and MS branches, so that HF features of PAN can reasonably be used to supplement that of MS. Experimental results on three datasets at both reduced- and full-resolution demonstrate the superiority of the proposed method compared with several state-of-the-art pansharpening models. The codes are available at https://***/YinghuiXing/FAFNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

Accuracy vs. Efficiency: Achieving both Through Hardware-Aware Quantization and Reconfigurable Architecture with Mixed Precision

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Accuracy vs. Efficiency: Achieving both Through Hardware-Awa...

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Libo Chang Shengbing Zhang Huimin Du Shiyu Wang Meikang Qiu Jihe Wang School of Computer Science Northwestern Polytechnical University and National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China School of Electronic Engineering Xi'an University of Posts and Telecommunication Xi'an China School of Computer Science Northwestern Polytechnical University and Engineering Research Center of Embedded System Integration Ministry of Education Xi'an China Texas A&M University-Commerce Texas USA

We propose a hardware/software co-design framework, which leverages hardware-aware quantization and a reconfigurable processor to improve the computational efficiency of convolutional neural networks (CNNs) on tiny IoT devices based on reconfigurable platforms. Firstly, we proposed a multi-objective optimization value function that can weigh accuracy, the size of CNN models, and computational delay, to improve the efficiency of the mixed- precision quantization algorithm based on deep reinforcement learning. Secondly, we propose a reconfigurable CNN processor that can adapt to the computing characteristics of various quantized CNN models, as well as a reconfigurable computing array and an on-chip elastic buffer, to improve the performance and computing efficiency on edge equipment. Finally, we demonstrate the effectiveness of the proposed co-design method through an extensive evaluation of the Ultra96-V2 platform. With respect to the well-known CNNs—VGG-16, ResNet-50, and MobileNet-V2, the experimental result shows that the throughput of 216.6 GOPS, 214.0 GOPS, and 53.6 GOPS, the computing efficiency of 0.63GOPS/DSP, 0.64GOPS/DSP, and 0.24 GOPS/DSP, respectively. In addition, achieving a better optimized trade-off between the computing efficiency and accuracy compared with the recently proposed CNN processor with fixed bit-width and mixed-precision.

关键词： Adaptation models Quantization (signal) Computational modeling Reinforcement learning Throughput System-on-chip Reconfigurable architectures

A novel weighted approach for time series forecasting based on visibility graph

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arXiv 2021年

作者： Zhan, Tianxiang Xiao, Fuyuan School of Computer and Information Science Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

Spectrometric Characterization of Suspension Liquid and Light Extinction Model Update

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SSRN

SSRN 2022年

作者： Liu, Sheng Shen, Yang Gao, Zihui Gao, Hongwen School of Computer Science and Technology Huaibei Normal University Huaibei235000 China State Key Laboratory of Enviromental Pollution Control and Resource Reuse Tongji University Shanghai200092 China Shanghai GreenEmpire Environmental Science and Technology Corporation Shanghai200092 China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

On the basis of the classical light scattering models, the light extinction model is the first to establish as [[EQUATION]] (, N and γ – average diameter in μm, number and relative refractive index of the suspending particles, λ , A and δ – incident light wavelength in μm, absorbance and optical path in cm of the suspension liquid) by spectrometric characterization of ten standard suspension liquids. It has been used to determine the suspending particles in the calcium oxalate, Formazine, soil, milk and sewage suspension water samples. As the result, the light extinction model method brought out less than 12% error of and 18% error of the suspending particles’ quality by comparing with the conventional methods. It provides a simple and reliable spectroptometric determination of a suspension liquid. Also, it is very potential to in-situ monitor the growth and working state of the suspending particles using in synthesis of materials, culture of cells, treatment of wastewater and safety of drinking water and foods. © 2022, The Authors. All rights reserved.

关键词： Suspensions (fluids)

A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions

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A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

International Symposium on Parallel and Distributed Processing with applications, ISPA

作者： Ye Yuan Mingxing Yang Ruoting Xiong Yani Sun Xianchao Zhang Wei Ren HuBei Land Resources Vocational College China Gemological Institute China University of Geosciences Wuhan China School of Computing Science University of East Anglia UK Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang University Kunming China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Beijing China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens; but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient.

关键词： Distributed processing Data privacy Manufacturing processes Information processing Handover Throughput Data structures Blockchains Delays Periodic structures