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Over-the-air federated learning: Status quo, open challenges, and future directions

Fundamental Research 2024年

作者： Xiao, Bingnan Yu, Xichen Ni, Wei Wang, Xin Poor, H. Vincent Key Laboratory for Information Science of Electromagnetic Waves (MoE) Department of Communication Science and Engineering Fudan University Shanghai 200433 China Data61 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sydney 2122 NSW Australia Department of Electrical and Computer Engineering Princeton University Princeton 08544 NJ United States

The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. The resulting demand for the aggregation of large amounts of data has caused serious communication bottlenecks in wireless networks and particularly at the network edge. Over-the-air federated learning (OTA-FL), leveraging the superposition feature of multi-access channels, enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. This paper provides a holistic review of progress in OTA-FL and points to potential future research directions. Specifically, we classify OTA-FL from the perspective of system settings, including single-antenna OTA-FL, multi-antenna OTA-FL, and OTA-FL with the aid of the emerging reconfigurable intelligent surface technology, and the contributions of existing works in these areas are summarized. Moreover, we discuss the trust, security and privacy aspects of OTA-FL, and highlight concerns arising from security and privacy. Finally, challenges and potential research directions are discussed to promote the future development of OTA-FL in terms of improving system performance, reliability, and trustworthiness. Specifical challenges to be addressed include model distortion under channel fading, the ineffective OTA aggregation of local models trained on substantially unbalanced data, and the limited accessibility and verifiability of individual local models. © 2024

关键词： Federated learning (FL) Machine learning (ML) Multiple-input multiple-out (MIMO) Over-the-air federated learning (OTA-FL) Privacy Reconfigurable intelligent surface (RIS) Security

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Provably Secure Authenticated key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

Energy Efficient Collaborative Federated Learning Design: A ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

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Trust Evaluation Model of Social Internet of Things Based on Multi-relationships

Trust Evaluation Model of Social Internet of Things Based on...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Fan, Fan Zhang, Hongbin Zhao, Dongmei Wang, Yanxia Liu, Bin Liu, Jian Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei Geological Workers' University Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Trust plays a vital role in ensuring the security of the Social Internet of Things. Due to the increased mobility of intelligent devices, there are frequent interactions between machines and changes in social relationships. It leads to a possible data sparsity problem in the Social Internet of Things. Therefore, it is presented that the trust evaluation model is based on various social relationships. The model considers the impact of multiple dynamically changing social relationships between nodes on trust. The implicit social relationship is mined based on the stated social relationship and potential attributes connecting nodes. It addresses the scarcity issue and the slow start of the Social Internet of Things. Simulations on the Santander Smart Cities dataset demonstrate that the approach improves the accuracy and convergence of trust evaluation in both sparse and regular networks. © 2022 IEEE.

关键词： Internet of things

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Research on Crowdsourcing Truth Inference Method Based on Graph Embedding

Research on Crowdsourcing Truth Inference Method Based on Gr...

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IEEE International Conference on Big knowledge (ICBK)

作者： Liangzhu Zhou Xingrui Zhuo Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score.

关键词： Crowdsourcing Training Graphical models Probabilistic logic Prediction algorithms Graph neural networks Robustness

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Design of Wireless data Center Network Structure Based on ExCCC-DCN 6th

Design of Wireless Data Center Network Structure Based on Ex...

引用

6th International Conference on data Science, ICDS 2019

作者： Jing, Yanhao Han, Zhijie Du, Xiaoyu Zhang, Qingfang School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

A data center is a cluster of servers, which is typically an organic collection of tens of thousands of servers. The sheer number of servers determines how well its performance is related to how it is interconnected. Just like the topology of Ethernet determines its network capacity and communication characteristics, the network structure of the data center has a great impact on its performance and capacity. The research object ExCCC-DCN in this paper has excellent node capacity, and its construction cost and operating energy consumption are relatively low, which is very suitable for deploying large-scale data centers. However, ExCCC-DCN is deployed on a wired link, and a large number of wired links can make maintenance and upgrade of the data center more difficult. Therefore, this paper utilizes the advantages of high transmission rate, strong anti-interference and high security of 60 GHz millimeter wave to wirelessly transform ExCCC-DCN, making it more flexible in the case of maintaining communication efficiency. © Springer Nature Singapore Pte Ltd 2020.

关键词： Millimeter waves

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Research on Mathematical Modelling and Analysis Mothod of Vehicle-bridge Coupled Vibration

Research on Mathematical Modelling and Analysis Mothod of Ve...

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2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Xu, Yichao Zhang, Jian Zhang, Yufeng Southeast University School of Civil Engineering Nanjing211189 China Jsti Group Nanjing211000 China State Key Laboratory of Safety and Health for In-Service Long Span Bridge Nanjing210012 China Ctr. of Data Measured by Struct. Health Monitoring System of Long-Span Bridge in Province of Jiangsu Nanjing210012 China

ISBN: (纸本)9781665409025

The existing vehicle-bridge coupled vibration analysis of the vehicle model cannot accurately consider the vehicle dynamic characteristics and the impact of flexible tires on the vehicle-bridge coupled vibration response. This paper analyzes the surface contact on the vehicle-bridge coupled vibration by establishing a new three-dimensional wheel model The contact surface between the wheel and the road surface is simulated as a rectangle, and the vehicle-bridge coupled vibration equation is established through the coordinated displacement conditions and force interaction between the contact surfaces. The vertical acceleration of bridges and car bodies is significantly affected by track irregularities. Improving line conditions can effectively improve the comfort of trains;at the same time, the higher the speed, the greater the root mean square of the random response of bridges and vehicles, which is caused by track irregularities. The greater the dispersion of the vibration response of the coupled system. © 2022 IEEE.

关键词： Big data

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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IEEE International Conference on Big knowledge (ICBK)

作者： Changyang Tai Ze Yang Huicheng Zhang Gongqing Wu Junwei Lv Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input.

关键词： Training knowledge engineering Deep learning Correlation Conferences Time series analysis data models

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PBCounter: Weighted Model Counting on Pseudo-Boolean Formulas

arXiv

引用

arXiv 2023年

作者： Lai, Yong Xu, Zhenghang Yin, Minghao College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Information Technology Northeast Normal University Changchun130017 China

In Weighted Model Counting (WMC), we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its literals. The current WMC solvers work on Conjunctive Normal Form (CNF) formulas. However, CNF is not a natural representation for human-being in many applications. Motivated by the stronger expressive power of pseudo-Boolean (PB) formulas than CNF, we propose to perform WMC on PB formulas. Based on a recent dynamic programming algorithm framework called ADDMC for WMC, we implement a weighted PB counting tool PBCounter. We compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD, ExactMC, D4, and ADDMC, where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF formula. The experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

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Color Measurement and Analysis of Unpacked Jujube in Shelf Life 11th

Color Measurement and Analysis of Unpacked Jujube in Shelf L...

引用

11th China Academic Conference on Printing and Packaging, CACPP 2020

作者： Yao, Danyang Yuan, Jiangping Xu, Xiangyang Chen, Guangxue State Key Laboratory of Pulp and Paper Engineering South China University of Technology Guangzhou China Institute for Visualization and Data Analysis Karlsruhe Institute of Technology Karlsruhe Germany School of Media and Communication Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9789811605024

Color attribute is an important indicator for the perception and evaluation of food quality, especially for unpacked valuable fruits. To this end, the color and weight evaluation of vitamin C enriched jujubes was carried out at constant temperature and humidity over the entire shelf life to provide a new pathway for their dynamic quality evaluation. Four unpackaged jujubes were examined with five random color points on the surface during the 11-days shelf-life. Spectral and colorimetric values of each jujube were recorded and correlated with its dynamic weight loss. Final, results showed that there was a significant linear correlation between the weight loss and the average chromatic aberration for each unpacked jujube. Meanwhile, the variable intervals of the spectral distribution were obviously distributed in the 630–690 nm band. Thus, specific spectral compensation can be verified for unpacked jujubes display in shelf life. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fruits

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