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FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning

FedHybrid: Hierarchical Hybrid Training for High-Performance...

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IEEE Communications Society conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Tao Chang Li Li Meihan Wu Wei Yu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China State Key Laboratory of Internet of Things for Smart City University of Macau China The 30th Research Institute of China Electronics Technology Group Corporation China

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same *** this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.

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Machine learning technique for wireless sensor networks 6

Machine learning technique for wireless sensor networks

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6th international conference on parallel, distributed and Grid computing, PDGC 2020

作者： Kaur, Rajwinder Kaur Sandhu, Jasminder Sapra, Luxmi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781728171326

Wireless Sensor networks comprise of various low-cost, low-energy sensor nodes that perform the data gathering task. In a network, data or packets are transferred from source to destination via sink node or other coordinating nodes. It can be outlined as a network of devices that communicate information collected from the sensor field. The information flow takes place with the help of wireless links. Sensors are normally qualified by limited interaction abilities because of power and bandwidth constraints. In this paper, the main focus is on network issues and their solution. We consider Machine Learning techniques implemented in this network to solve some network problems. Machine Learning is the process where we train the model or machine based on training data, the model is programmed in such a way so that it 'learns' from the information that it holds. This paper contains details of publications spanning a period of 2015-2020 for Machine Learning techniques that describe the challenging issues of Wireless Sensor Network. © 2020 IEEE.

关键词： Anomaly detection

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distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation networks Based on Automatic LSTM Customization and Sharing 26th

Distributed Fine-Grained Traffic Speed Prediction for Large-...

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26th international conference on parallel and distributed computing (Euro-Par)

作者： Lee, Ming-Chang Lin, Jia-Chun Gran, Ernst Gunnar Norwegian Univ Sci & Technol Dept Informat Secur & Commun Technol N-2815 Gjovik Norway Simula Res Lab N-1364 Fornebu Norway

ISBN: (纸本)9783030576752;9783030576745

Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for large-scale transportation networks where numerous traffic detectors are deployed has not been well studied. In this paper, we propose DistPre, which is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. To achieve fine-grained and accurate traffic-speed prediction, DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector. To make such a customization process efficient and applicable for large-scale transportation networks, DistPre conducts LSTM customization on a cluster of computation nodes and allows any trained LSTM model to be shared between different detectors. If a detector observes a similar traffic pattern to another one, DistPre directly shares the existing LSTM model between the two detectors rather than customizing an LSTM model per detector. Experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistPre. The results demonstrate that DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.

关键词： Hyperparameter tuning Lightweight LSTM Large-scale transportation networks Traffic speed prediction distributed and parallel processing The Nelder-Mead method

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Deep Learning Used to Recognition Swimmers Drowning

Deep Learning Used to Recognition Swimmers Drowning

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

作者： Jia-Xian Jian Chuin-Mu Wang National Chin-Yi University of Technology Computer Science and Information Engineering Taichung City Taiwan

ISBN: (纸本)9781665447324

Many people believe that when drowning occurs, there will be calls for help. In fact, people who are drowning do not get too many splashes or cry for help. They only try to get themselves out of the water by treading on the water. The drowning condition may cause serious brain damage, so it is extremely important to shorten the time it takes to detect the occurrence of drowning and *** paper proposes using computer image processing technology to introduce artificial intelligence motion technology, mounting the camera on the bottom of the swimming pool, and use OpenPose to mark the image joint point features, and input the captured joint point features into the recursive neural network to determine whether the swimmer is drowning. The final training result is about 89.4% accurate, so it can be used to assist on-site lifeguards to detect swimmers who may be drowning, and to reduce incidents that cannot be detected immediately

关键词： Training Image recognition Recurrent neural networks Computational modeling Feature extraction Skeleton Artificial intelligence

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Text sentiment classification based on BP neural network

Text sentiment classification based on BP neural network

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Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Nanchang Cheng Wenchao Soong Kang Song National Broadcast Media Language Resources Monitoring & Research Center Communication University of China Beijing China

In a consumer-oriented market, people's opinions or comments will, directly or indirectly, have influence on other people's decisions. For example, as choosing goods or online services, positive opinions or comments can promote consumers' purchase. At the same time, negative comments can also reduce consumers' enthusiasm for buying. The BP neural network will be trained with a supervised learning method, and finally obtain the ability that can classify positive or negative text reviews. In this paper, the supervised training method is adopted to improve the operation speed of the whole model by 8-9 times after optimizing the data and structure. The accuracy rate, recall rate, and F1 value are around 87.5%, 86.7%, and 87.35% respectively.

关键词： Training Neural networks Software algorithms Text categorization Supervised learning Data models Classification algorithms

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Dependency-Aware Hybrid Task Offloading in Mobile Edge computing networks

Dependency-Aware Hybrid Task Offloading in Mobile Edge Compu...

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international conference on parallel and distributed Systems (ICPADS)

作者： Zhao Ming Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China The University of British Columbia Vancouver Canada

ISBN: (纸本)9781665408790

With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs.

关键词： Energy consumption Multi-access edge computing NP-hard problem Simulation Mobile handsets Delays Device-to-device communication

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Optimization of RDMA-Based HDFS Data Distribution Mechanism 17th

Optimization of RDMA-Based HDFS Data Distribution Mechanism

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17th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2020

作者： Zhang, Xiao Liu, Binbin Zhao, Junhao Dong, Cong School of Computer Science Northwestern Polytechnical University Xian Shaanxi710129 China

ISBN: (纸本)9783030794774

Hadoop distributed File System (short for HDFS) is a high availability file system designed to run on commodity hardware. It uses multiple replicas to ensure high reliability, and many data are transmitted between storage nodes. The performance of data transmission has a great impact on the latency of writing operations. Remote Direct Memory Access (short for RDMA) is a protocol with low latency and high through which is running on the Infiniband network. When HDFS runs on the Infiniband network, the default protocol IPoIB can not take advantage of the high-speed network. The latency of the writing process is similar to a TCP/IP network. In this paper, we present a new RDMA-based HDFS writing mechanism. It enables DataNodes to read data parallelly from the Client through RDMA. And by using RDMA primitive, the transmission latency is slower than the original TCP/IP protocol. The experiments show that our approach reduces the latency of the writing process by 10.11%–40.81% compared with the original HDFS. © 2021, IFIP international Federation for Information Processing.

关键词： HIgh speed networks

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Collar Data Synthesis and Its Application for Generating Virtual Collar 3D Novel View from a Single-View

Collar Data Synthesis and Its Application for Generating Vir...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Na Guo Pei-Wei Tsai Hongwang Xiao Chang Wu Wenjuan Duan Department of Computing Technologies Swinburne University of Technology Melbourne Australia School of Mechanical Electrical &Information Engineering Shandong University Weihai Weihai China Beijing Academy Artificial Intelligence Beijing China Department of Computing Technologies Beijing Institute of Technology Beijing China

In recent years, virtual reality, big data and cloud computing technologies have all made rapid development, and they are also more and more closely combined with the apparel industry. The apparel digital design and virtual fitting techniques are playing increasingly important roles in the field of apparel design, manufacturing and sales. The three-dimensional digital design and virtual fitting make the designers or the users have multi-view and more real impressions and experiences of the apparel. But 3D display is difficult to be popularized in apparel industry because of its high cost. The cost of two-dimensional display is low and easy to popularize. However, 2D system can only provide images of a specific perspective, lacking a multi-perspective experience. If the supplier is required to provide 2D images of several specific perspectives, the personalized needs of users cannot be satisfied. Therefore, how to make 2D system according to an object image and users' needs to provide the object images of other perspectives is an urgent problem to be solved. In this work, we propose a novel view synthesis pipeline based on an enhanced Pix2Pix neural network for a novel view (Pix2Pix-V). It consists of four parts: 1) a new method of generating the labeled virtual collar image data based on its 3D model, and the 2D images can be obtained on any perspective; 2) Pix2Pix-V neural network; 3) a Pix2Pix-V training workflow for predicting the novel view; 4) transfer learning is used in predicting similar views of virtual collars.

关键词： Industries Solid modeling Three-dimensional displays Costs Pipelines Transfer learning Neural networks

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An Electromagnetic Covert Channel based on Neural Network Architecture

An Electromagnetic Covert Channel based on Neural Network Ar...

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international conference on parallel and distributed Systems (ICPADS)

作者： Chaojie Gu Jiale Chen Rui Tan Linshan Jiang College of Control Science and Engineering Zhejiang University China The School of Computer Science and Engineering Nanyang Technological University. School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665408790

Outsourcing the design of deep neural networks may incur cybersecurity threats from the hostile designers. This paper studies a new covert channel attack that leaks the inference results over the air through a hostile design of the neural network architecture and the computing device's electromagnetic radiation when executing the neural network. Specifically, the hostile neural network consists of a series of binary models that correspond to all classes and are executed sequentially. The execution terminates once any binary model given the input is positive about its responsible class. We describe an approach to generate such binary models by pruning a benign neural network that is trained using the standard method to deal with all the classes. Compared with the benign neural network, the hostile one has similar memory usage and negligible classification accuracy drop, but distinct inference times for the samples of different classes. As a result, the hostile neural network's classification result can be eavesdropped by measuring the duration of the electromagnetic radiation emanated from the computing device. As neural networks are stored and transmitted as data files, this covert channel attack is more stealthy to the anti-malware than other code-based attacks. We implement the described attack on two edge computing devices that execute the hostile neural network on CPU or GPU. Evaluation shows 100% empirical accuracy in eavesdropping the inference results.

关键词： Computational modeling Atmospheric modeling Power system management Neural networks Computer architecture Receivers Electromagnetic radiation

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SparsePipe: parallel Deep Learning for 3D Point Clouds 27

SparsePipe: Parallel Deep Learning for 3D Point Clouds

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27th IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Zhai, Keke He, Pan Banerjee, Tania Rangarajan, Anand Ranka, Sanjay Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)9781665422925

We propose SparsePipe, an efficient and asynchronous parallelism approach for handling 3D point clouds with multi-GPU training. SparsePipe is built to support 3D sparse data such as point clouds. It achieves this by adopting generalized convolutions with sparse tensor representation to build expressive high-dimensional convolutional neural networks. Compared to dense solutions, the new models can efficiently process irregular point clouds without densely sliding over the entire space, significantly reducing the memory requirements and allowing higher resolutions of the underlying 3D volumes for better performance. SparsePipe exploits intra-batch parallelism that partitions input data into multiple processors and further improves the training throughput with inter-batch pipelining to overlap communication and computing. Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead. Using experimental results on an eight-GPU platform, we show that SparsePipe can parallelize effectively and obtain better performance on current point cloud benchmarks for both training and inference, compared to its dense solutions.

关键词： asynchronous distributed training model parallelism load balancing sparse DNN 3D point clouds

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