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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of

Xiaoming Yuan,Jiahui Chen,Ning Zhang,Qiang Ye,Changle Li,Chunsheng Zhu,Xuemin Sherman Shen,

Engineering doi: 10.1016/j.eng.2023.04.015

Abstract: In order to protect data privacy and improve data learning efficiency in knowledge sharing, we proposean asynchronous federated broad learning (FBL) framework that integrates broad learning (BL) into federatedlearning (FL).Based on the results of resource scheduling, we design a reward-allocation algorithm based on federatedincentive learning (FIL) in FBL to compensate clients for their costs.

Keywords： Knowledge sharing     Internet of Vehicles     Federated learning     Broad learning     Reconfigurable intelligent surfaces    

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Federated unsupervised representation learning Research Article

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1181-1193 doi: 10.1631/FITEE.2200268

Abstract: enormous amount of unlabeled data on distributed edge devices, we formulate a new problem in called federatedTo address these challenges, we propose the federated contrastive averaging with dictionary and alignment

Keywords： Federated learning     Unsupervised learning     Representation learning     Contrastive learning    

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Federated Learning for 6G: Applications, Challenges, and Opportunities Review

Zhaohui Yang, Mingzhe Chen, Kai-Kit Wong, H. Vincent Poor, Shuguang Cui

Engineering 2022, Volume 8, Issue 1,   Pages 33-41 doi: 10.1016/j.eng.2021.12.002

Abstract:

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and inference.One approach to mitigate these problems is federated learning (FL), which enables the devices to traina common machine learning model without data sharing and transmission.

Keywords： Federated learning 6G     Reconfigurable intelligent surface     Semantic communication     Sensing     communication    

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Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and Research Article

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1390-1402 doi: 10.1631/FITEE.2300098

Abstract: (FL) is a novel technique in deep learning that enables clients to collaboratively train a shared modelFurthermore, we propose a novel framework called federated mutual learning (FML), which enables eachWe introduce a technique called deep mutual learning (DML) to transfer knowledge between these two modelsincludes only certain parts, and the personalized model is task-specific and enhanced through mutual learning

Keywords： Federated learning     Knowledge distillation     Privacy preserving     Heterogeneous environment    

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Training time minimization for federated edge learning with optimized gradient quantization and bandwidth Research Article

Peixi LIU, Jiamo JIANG, Guangxu ZHU, Lei CHENG, Wei JIANG, Wu LUO, Ying DU, Zhiqin WANG,jiangjiamo@caict.ac.cn,gxzhu@sribd.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1247-1263 doi: 10.1631/FITEE.2100538

Abstract: Training a machine learning model with (FEEL) is typically time consuming due to the constrained computationWith different learning tasks and models, the validation of our analysis and the near-optimal performance

Keywords： Federated edge learning     Quantization optimization     Bandwith allocation     Training time minimization    

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Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT Article

Qian Wang, Siguang Chen, Meng Wu

Engineering 2023, Volume 31, Issue 12,   Pages 127-138 doi: 10.1016/j.eng.2022.10.014

Abstract: Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithmMeanwhile, a contribution-based federated aggregation method is developed, in which the aggregation weights

Keywords： Computation offloading     Caching     Incentive     Blockchain     Federated deep reinforcement learning    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

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Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

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Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2

Abstract: In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstratedMachine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThis paper primarily aims to review the research and application of machine learning in the field ofThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelydiagnosis under actual operating conditions are revealed, and the future research trends of machine learning

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predictdecision support for moving trajectory control and serve as a foundation for the application of deep learning

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Frontiers of Medicine 2023, Volume 17, Issue 4,   Pages 768-780 doi: 10.1007/s11684-023-0982-1

Abstract: illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learningMachine learning modeling based on personal whole-exome data identified 46 genes with mutation burden

Keywords： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Title Author Date Type Operation

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of

Xiaoming Yuan,Jiahui Chen,Ning Zhang,Qiang Ye,Changle Li,Chunsheng Zhu,Xuemin Sherman Shen,

Journal Article

Federated unsupervised representation learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Journal Article

Federated Learning for 6G: Applications, Challenges, and Opportunities

Zhaohui Yang, Mingzhe Chen, Kai-Kit Wong, H. Vincent Poor, Shuguang Cui

Journal Article

Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and

Tao SHEN, Jie ZHANG, Xinkang JIA, Fengda ZHANG, Zheqi LV, Kun KUANG, Chao WU, Fei WU,chao.wu@zju.edu.cn,wufei@zju.edu.cn

Journal Article

Training time minimization for federated edge learning with optimized gradient quantization and bandwidth

Peixi LIU, Jiamo JIANG, Guangxu ZHU, Lei CHENG, Wei JIANG, Wu LUO, Ying DU, Zhiqin WANG,jiangjiamo@caict.ac.cn,gxzhu@sribd.cn

Journal Article

Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

Qian Wang, Siguang Chen, Meng Wu

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article