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Department: search.filters.department.Department of Electrical and Electronics EngineeringSubject: search.filters.subject.Internet of things

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    Data-agnostic model poisoning against federated learning: a graph autoencoder approach
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Li, Kai; Zheng, Jingjing; Yuan, Xin; Ni, Wei; Poor, H. Vincent; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Department of Electrical and Electronics Engineering;  ; College of Engineering;  
    This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL. © 2005-2012 IEEE.
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    Federated learning for pedestrian detection in vehicular networks
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bennis, Mehdi; Elgabli, Anis; Gündüz, Deniz; Karaağaç, Sercan; Department of Electrical and Electronics Engineering; Kümeç, Feyzi Ege; Reyhanoğlu, Aslıhan; Kar, Emrah; Turan, Buğra; Ergen, Sinem Çöleri; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL)
    Vehicular connectivity is foreseen to increase road safety by enabling connected vehicle applications. On the other hand, machine learning (ML) methods are provisioned to increase road safety by supporting object detection and assisted driving. Recently, distributed ML methods, which rely on data transmission between a parameter server and vehicular edge devices, are introduced to develop intelligent transportation systems. In this paper, we investigate the feasibility of the usage of a distributed ML algorithm, federated learning (FL), to detect pedestrians by using vehicular networks. We first provide a comprehensive overview of the proposed scheme, then highlight the methodology to enable FL-based pedestrian detection from the images obtained by vehicle cameras. We further present experimental validation results for communication resource utilization, and pedestrian detection accuracy by using convolutional neural networks (CNNs) and deep neural networks (DNNs) layers in our model architecture for an FL scheme. We obtain 90% pedestrian detection accuracy with our FL scheme. © 2023 IEEE.
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    Flexible and cognitive radio access technologies for 5G and beyond
    (Institution of Engineering and Technology, 2020) Arslan, Hüseyin; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116
    Standards for 5G and beyond will require communication systems with a much more flexible and cognitive design to support a wide variety of services including smart vehicles, smart cities, smart homes, IoTs, and remote health. Although future 6G technologies may look like an extension of their 5G counterparts, new user requirements, completely new applications and use-cases, and networking trends will bring more challenging communication engineering problems. New communication paradigms in different layers will be required, in particular in the physical layer of future wireless communication systems. This comprehensive book is intended to be both a tutorial on flexible and cognitive radio access technologies for 5G and beyond and an advanced overview for technical professionals and managers in the communications industry, as well as researchers in academia and industry. The authors cover enabling radio access technologies for 5G and beyond, not only from a standard specific angle (like 5G) but also by considering future trends beyond 5G. Rather than specific standard implementations, the book covers a wide variety of technologies and their uses. The presentations are both descriptive and mathematical in nature to cater to readers who need mathematical description as well as readers who do not. The book is written at a level suited to readers who already have a background in electrical engineering and basic wireless communications.