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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints 24

Joint Sparsification and Quantization for Wireless Federated...

引用

24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

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

ISBN: (纸本)9781665496261

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. © 2023 IEEE.

关键词： Budget control

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Stealthy Backdoor Attack Towards Federated Automatic Speaker Verification

Stealthy Backdoor Attack Towards Federated Automatic Speaker...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Longling Zhang Lyqi Liu Dan Meng Jun Wang Shengshan Hu School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security OPPO Research Institute China

Automatic speech verification (ASV) authenticates individuals based on distinct vocal patterns, playing a pivotal role in many applications such as voice-based unlocking systems for devices. The ASV system comprises three stages: training, registration, and validation. The model refines using voice data in training, extracts vocal features in registration, and contrasts these with speech patterns in validation. Modern ASV models, primarily grounded in DNN architectures, require extensive data for training. Federated learning (FL) fosters model-sharing across multiple clients while ensuring data privacy. Due to its open architecture, FL is vulnerable to backdoor attacks. However, training a stealthy backdoor attack in FL presents challenges, including diminished attack generalization owing to data heterogeneity, and conspicuous triggers that render them easily detectable. In this paper, we propose a Federated Stealthy Backdoor Attack method ($FedSBA$). FedSBA aims to improve the attack model’s generalization, enhance its persistence, and elude anomaly detection under the heterogeneous data distribution. FedSBA constructs an attack model based on a personalized transformer and encompasses a stealthy trigger. Moreover, we also propose a defensive strategy that utilizes an adaptive weight aggregation scheme. The stealthiness and effectiveness of FedSBA are demonstrated by exhibiting superior performance in comparison to previous works.

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Diabetes Mellitus Type 2 data Sharing system Based on Blockchain and Attribute-Encryption

Diabetes Mellitus Type 2 Data Sharing System Based on Blockc...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Weiqi Dai Zhenhui Lu Xia Xie Duoqiang Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

Medical data sharing can improve the quality of medical care and promote progress in the field of public health. However, medical data has high confidentiality and complexity. Taking the clinical diagnosis and treatment data of Diabetes Mellitus Type 2 (T2DM) in medical treatment as an example, T2DM data often has the phenomenon of "multi-disease coexistence" and is insecure and inaccurate in the process of sharing. Based on Blockchain and Attribute- Encryption (ABE), we propose a T2DM data sharing system to solve the above problems. An Attribute-Encryption algorithm T2DM-CP-ABE is proposed to solve the problem of excessive sharing granularity. T2DM-CP-ABE, combined with the medical data contribution module, takes the contribution factor as an important consideration, to stimulate the power of T2DM data sharing and promote the development of national diabetes prevention. Based on the Blockchain, a medical data attribute authorization model is proposed, which provides a credible solution for multiple hospitals to realize medical data attribute authorization, and realizes the clinical diagnosis and treatment data sharing of T2DM among doctors. Based on Blockchain and ABE technology, a data sharing system for T2DM has been implemented on the Fabric platform. The result shows that the average time for the system to complete a single sharing is less than 1 second.

关键词： Authorization Hospitals Fabrics Blockchains Diabetes Encryption Security

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Shared Incentive system for Clinical Pathway Experience

Shared Incentive System for Clinical Pathway Experience

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Weiqi Dai Wenhao Zhao Xia Xie Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

The phenomenon of unbalanced regional medical resources has led to large differences in the implementation experience of clinical pathways in hospitals with different medical levels. However, due to the fear of privacy leakage and the lack of sharing motivation, there is a lack of effective collaborative communication channels for clinical pathways among medical institutions. In response to these problems, we propose a blockchain-based sharing incentive scheme for clinical pathway experience, which uses the clinical pathway implementation effect evaluation system to evaluate the quality of clinical pathway experience data. Specifically, we designe a two-phase cross-domain shared transaction model for the transaction of clinical pathway experience data. Moreover, we introduce the shared transaction alliance committee to verify and review the transaction, and solve the problems in the transaction in the arbitration phase. Finally, the functional test results show that the system meets the experience sharing incentive requirements, and the performance test results show that the TPS of sharing clinical pathway experience read and writed can reach around 600 and 1000, and the transaction latency of each phase is within 3 s.

关键词： Privacy Hospitals Collaboration Communication channels data models Security Incentive schemes

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A Two-Stage Missing Value Imputation Method Based on Autoencoder Neural Network

A Two-Stage Missing Value Imputation Method Based on Autoenc...

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IEEE International Conference on big data

作者： Jiayin Yu Yulin He Joshua Zhexue Huang College of Computer Science & Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9781665445993

Due to the ubiquitous presence of missing values in real-world datasets, an imputation algorithm can recover the missing values and provide users with a complete dataset that utilizes all the available observed information. However, most of the imputation methods still have several limitations, including cannot restore the original distribution, handling various data missing patterns, and high missing rate dataset. In this poster, a novel neural network-based two-stage missing value imputation (abbreviated as TS-MVI) method is proposed to fill an incomplete condition attribute with the optimized attribute values for the supervised learning task. By initializing the missing values with random numbers, the imputation values are iteratively adjusted based on the new updating rule by minimizing both the autoencoder-oriented objective function and neural network-based classification error. The persuasive experiments show that TS-MVl method significantly outperforms current state-of-the-art imputation methods and thus demonstrate TS-MVI is a viable approach to deal with the missing value imputation problem.

关键词： Conferences Supervised learning Neural networks big data Linear programming Task analysis

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Random Sample Partition-Based Clustering Ensemble Algorithm for big data

Random Sample Partition-Based Clustering Ensemble Algorithm ...

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IEEE International Conference on big data

作者： Xueqin Du Yulin He Joshua Zhexue Huang College of Computer Science & Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9781665445993

A novel random sample partition-based clustering ensemble (RSP-CE) algorithm is proposed in this paper to handle the big data clustering problems. There are three key components in RSP-CE algorithm, i.e., generating the base clustering results on RSP data blocks, harmonizing the based clustering results with maximum mean discrepancy (MMD) criterion, and refining the RSP clustering results. RSP data blocks have the consistent sample distributions with the whole big data and thus provide the possibility for using base clustering results on different data subsets to approximate the clustering result on whole big data. The experimental results in comparison with other 5 well-known clustering ensemble algorithms on 4 big data sets show that RSP-CE algorithm obtains the better normalized mutual information (NMI) values and Fowlkes-Mallows Index (FMI) values with the less training time consumptions and thus demonstrate that RSP-CE algorithm is a viable approach to deal with the big data clustering problems.

关键词： Training Conferences Refining Clustering algorithms big data Approximation algorithms Partitioning algorithms

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Whale: Efficient One-to-Many data Partitioning in RDMA-Assisted Distributed Stream Processing systems

Whale: Efficient One-to-Many Data Partitioning in RDMA-Assis...

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Supercomputing Conference

作者： Jie Tan Hanhua Chen Yonghui Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

To process large-scale real-time data streams, existing distributed stream processing systems (DSPSs) leverage different stream partitioning strategies. The one-to-many data partitioning strategy plays an important role in various applications. With one-to-many data partitioning, an upstream processing instance sends a generated tuple to a potentially large number of downstream processing instances. Existing DSPSs leverage an instance-oriented communication mechanism, where an upstream instance transmits a tuple to different downstream instances separately. However, in one-to-many data partitioning, multiple downstream instances typically run on the same machine to exploit multi-core resources. Therefore, a DSPS actually sends a data item to a machine multiple times, raising significant unnecessary costs for serialization and communication. We show that such a mechanism can lead to serious performance bottleneck due to CPU overload. To address the problem, we design and implement Whale, an efficient RDMA (Remote Direct Memory Access) assisted distributed stream processing system. Two factors contribute to the efficiency of this design. First, we propose a novel RDMA-assisted stream multicast scheme with a self-adjusting non-blocking tree structure to alleviate the CPU workloads of an upstream instance during one-to-many data partitioning. Second, we re-design the communication mechanism in existing DSPSs by replacing the instance-oriented communication with a new worker-oriented communication scheme, which saves significant costs for redundant serialization and communication. We implement Whale on top of Apache Storm and conduct comprehensive experiments to evaluate its performance with large-scale real world datasets. The results show that Whale achieves 56.6x improvement of system throughput and 97% reduction of processing latency compared to existing designs.

关键词： Costs Storms High performance computing Memory management Whales Distributed databases Throughput

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Robust UAV Policy Learning for Urban Infrastructure Surface Screening*

Robust UAV Policy Learning for Urban Infrastructure Surface ...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Bingqing Du Uddin Md. Borhan Tianxing Chen Jianyong Chen Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal tools for infrastructure inspection, mitigating risks to human life and enhancing operational efficiency. Despite advancements, achieving autonomous surface coverage inspection in GPS-denied environments remains a significant challenge. This paper presents a novel approach to enhance UAV policy learning for surface screening of urban infrastructure in environments lacking GPS. Utilizing Deep Reinforcement Learning (DRL), the methodology enables UAVs to autonomously conduct surface coverage inspections more robustly and efficiently. A key innovation is the Optimal Speed Decision (OSD) model, which allows UAVs to adjust flight speed dynamically based on scene perception, enhancing efficiency and safety. Additionally, an Off-Policy Critic-Based Reward Shaping (OPCBRS) method is introduced to improve learning efficiency and performance by addressing the issue of sparse rewards using offline data. Simulation results confirm the effectiveness of these methods, with the OSD model demonstrating significant improvements in navigating complex scenarios and maintaining a balance between efficiency and robustness over traditional uniform speed policies. This research offers a robust framework for UAV-based infrastructure inspection in GPS-denied environments, advancing autonomous UAV applications.

关键词： Technological innovation Navigation Simulation Inspection Autonomous aerial vehicles Deep reinforcement learning Robustness Real-time systems Safety Robots

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Research on the Application of Business data Fusion in the engineering Management of Oil and Gas Storage and Transportation Station 4

Research on the Application of Business Data Fusion in the E...

引用

4th International Conference on Computer Science and Management technology, ICCSMT 2023

作者： Xie, Rui Fan, Bo Ma, Jianlin Pan, Meng Southwest Pipeline Ltd. PipeChina ChengDu610213 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu610031 China Chengdu Aerospace Science and Industry Big Data Institute Ltd. Casic Chengdu610213 China National Engineering Laboratory of Industrial Big-Data Application Technology Casic Chengdu610218 China

ISBN: (纸本)9798400709517

Due to challenges such as complex contractor personnel, a large amount of scattered form data, a cumbersome business implementation process, low levels of digitalization and security, and difficult management in the engineering management business of oil and gas storage and transportation stations, this paper proposes a study on business data fusion application in the engineering management of oil and gas storage and transportation stations. Focused on the business processes and data of engineering management in oil and gas storage and transportation stations, this research establishes and analyzes an architecture for engineering management business in these stations. Using an Artifact-centric data fusion method, the research constructs an information model for the engineering management business, identifying the data topics for this area. Finally, the research applies and verifies the study of business data fusion through real-world business management scenarios. As researchers in the field of business data fusion, this study utilizes digital technologies and methods to enhance the overall safety management of engineering operations, thereby promoting the transformation and upgrading of engineering management business in oil and gas storage and transportation stations © 2023 ACM.

关键词： Information management

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Weiming Du Junmou Lin Binqing Du Uddin Md. Borhan Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate.

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