Special lssues
Table of Content

Federated Learning Algorithms, Approaches, and Systems for Internet of Things

Submission Deadline: 30 April 2023 (closed)

Guest Editors

Prof. Mu Zhou, Chongqing University of Posts and Telecommunications, China
Prof. Ying-Ren Chien, National Ilan University, Taiwan
Prof. Xin Liu, Dalian University of Technology, China

Summary

This special issue focuses on algorithms, approaches, and systems based on federated learning for the Internet of Things (IoT) in the smart industry, smart transportation, and smart healthcare, etc. With the development of the IoT, it has ushered in the explosive growth of data and the rapid development of machine learning. However, this creates data security and privacy issues while providing convenient services, and federated learning comes into being. Federated learning is essentially a distributed machine learning technique, or machine learning framework. The goal of federated learning is to achieve joint modeling and improve the effect of Artificial Intelligence (AI) models on the basis of ensuring data privacy security and legal compliance. On the premise of ensuring information security, terminal data privacy, and personal data privacy during data exchange, federated learning can perform high-efficiency machine learning among multiple computing nodes, and is expected to become the basis of the next generation of artificial intelligence collaborative algorithms and collaborative networks. This special issue aims to explore federated learning algorithms, approaches, and systems for the IoT, and provide high-quality IoT services while protecting data privacy and information security. Potential topics include but are not limited to the following:

— Federated learning for IoT data sharing, offloading, and caching

— Federated learning for IoT attack detection

— Federated learning for IoT mobile crowd-sensing

— Federated learning for IoT localization and tracking

— Federated learning for IoT security and privacy

— Federated learning for data-driven IoT systems

— Federated learning for IoT on the blockchain

— The combination of federated learning and distributed machine learning

— Applications of federal learning in city and industry intelligentization


Keywords

Federated learning; Artificial Intelligence; Internet of Things; wireless networks; machine learning; data privacy; information security

Published Papers


  • Open Access

    REVIEW

    AI Fairness–From Machine Learning to Federated Learning

    Lalit Mohan Patnaik, Wenfeng Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1203-1215, 2024, DOI:10.32604/cmes.2023.029451
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract This article reviews the theory of fairness in AI–from machine learning to federated learning, where the constraints on precision AI fairness and perspective solutions are also discussed. For a reliable and quantitative evaluation of AI fairness, many associated concepts have been proposed, formulated and classified. However, the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness. The privacy worries induce the data unfairness and hence, the biases in the datasets for evaluating AI fairness are unavoidable. The imbalance between algorithms’ utility and humanization More >

  • Open Access

    ARTICLE

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

    Sanxiu Jiao, Lecai Cai, Xinjie Wang, Kui Cheng, Xiang Gao
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1679-1694, 2024, DOI:10.32604/cmes.2023.030512
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection… More >

    Graphic Abstract

    A Differential Privacy Federated Learning Scheme Based on Adaptive Gaussian Noise

  • Open Access

    ARTICLE

    Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

    Tao Yin, Changgen Peng, Weijie Tan, Dequan Xu, Hanlin Tang
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 827-843, 2024, DOI:10.32604/cmes.2023.029039
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract In the assessment of car insurance claims, the claim rate for car insurance presents a highly skewed probability distribution, which is typically modeled using Tweedie distribution. The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset, when the data is provided by multiple parties, training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge. To address this issue, this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos. The algorithm can keep sensitive data locally and uses… More >

  • Open Access

    ARTICLE

    Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

    Zekai Li, Wenfeng Wang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s. Consequently, the broad federated More >

  • Open Access

    ARTICLE

    Single Image Deraining Using Dual Branch Network Based on Attention Mechanism for IoT

    Di Wang, Bingcai Wei, Liye Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1989-2000, 2023, DOI:10.32604/cmes.2023.028529
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract Extracting useful details from images is essential for the Internet of Things project. However, in real life, various external environments,such as badweather conditions,will cause the occlusion of key target information and image distortion, resulting in difficulties and obstacles to the extraction of key information, affecting the judgment of the real situation in the process of the Internet of Things, and causing system decision-making errors and accidents. In this paper, we mainly solve the problem of rain on the image occlusion, remove the rain grain in the image, and get a clear image without rain. Therefore,… More >

  • Open Access

    ARTICLE

    Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles

    Bingcai Wei, Di Wang, Zhuang Wang, Liye Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1975-1988, 2023, DOI:10.32604/cmes.2023.027727
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract With the increasing popularity of artificial intelligence applications, machine learning is also playing an increasingly important role in the Internet of Things (IoT) and the Internet of Vehicles (IoV). As an essential part of the IoV, smart transportation relies heavily on information obtained from images. However, inclement weather, such as snowy weather, negatively impacts the process and can hinder the regular operation of imaging equipment and the acquisition of conventional image information. Not only that, but the snow also makes intelligent transportation systems make the wrong judgment of road conditions and the entire system of… More >

    Graphic Abstract

    Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles

  • Open Access

    ARTICLE

    A Client Selection Method Based on Loss Function Optimization for Federated Learning

    Yan Zeng, Siyuan Teng, Tian Xiang, Jilin Zhang, Yuankai Mu, Yongjian Ren, Jian Wan
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 1047-1064, 2023, DOI:10.32604/cmes.2023.027226
    (This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
    Abstract Federated learning is a distributed machine learning method that can solve the increasingly serious problem of data islands and user data privacy, as it allows training data to be kept locally and not shared with other users. It trains a global model by aggregating locally-computed models of clients rather than their raw data. However, the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model. For this problem, we focus on the client selection with federated learning, which can affect the convergence performance of the… More >

    Graphic Abstract

    A Client Selection Method Based on Loss Function Optimization for Federated Learning

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