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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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    FLAGS simulation framework for federated learning algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2023) Department of Computer Engineering; Lodhi, Ahnaf Hannan; Shamsizade, Toghrul; Al Asaad, Omar Mohammad; Akgün, Barış; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering
    Federated Learning (FL) provides an effective mechanism for distributed learning. However, it is expected to operate in a highly diverse setting with distinct behaviors from the participating nodes as well as dynamic network conditions. The FL performance, therefore, is subject to change due to the highly transitory nature of the overall system. An efficient simulation framework must be flexible to allow a range of participant behaviors, interactions, and environment characteristics. In this demo paper, we present the Federated Learning Algorithm Simulation (FLAGS) framework that we propose as a lightweight FL implementation and testing platform. FLAGS framework allows for a wide range of device behaviors and cooperative mechanisms, enabling rapid testing of multiple FL algorithms. © 2023 IEEE.
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    PublicationOpen Access
    M-stability: threshold security meets transferable utility
    (Association for Computing Machinery (ACM), 2021) Department of Computer Engineering; Biçer, Osman; Küpçü, Alptekin; Yıldız, Burcu; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 168060; N/A
    Use of game theory and mechanism design in cloud security is a well-studied topic. When applicable, it has the advantages of being efficient and simple compared to cryptography alone. Most analyses consider two-party settings, or multi-party settings where coalitions are not allowed. However, many cloud security problems that we face are in the multi-party setting and the involved parties can almost freely collaborate with each other. To formalize the study of disincentivizing coalitions from deviating strategies, a well-known definition named k-resiliency has been proposed by Abraham et al. (ACM PODC '06). Since its proposal, k-resiliency and related definitions are used extensively for mechanism design. However, in this work we observe the shortcoming of k-resiliency. That is, although this definition is secure, it is too strict to use for many cases and rule out secure mechanisms as insecure. To overcome this issue, we propose a new definition named ?.,""-repellence against the presence of a single coalition to replace k-resiliency. Our definition incorporates transferable utility in game theory as it is realistic in many distributed and multi-party computing settings. We also propose m-stability definition against the presence of multiple coalitions, which is inspired by threshold security in cryptography. We then show the advantages of our novel definitions on three mechanisms, none of which were previously analyzed against coalitions: incentivized cloud computation, forwarding data packages in ad hoc networks, and connectivity in ad hoc networks. Regarding the former, our concepts improve the proposal by Küpçü (IEEE TDSC '17), by ensuring a coalition-proof mechanism.
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    Moderated redactable blockchains: a definitional framework with an efficient construct
    (Springer Science and Business Media Deutschland GmbH, 2020) Dousti, M. S.; Department of Computer Engineering; Küpçü, Alptekin; Faculty Member; Department of Computer Engineering; College of Engineering; 168060
    Blockchain is a multiparty protocol to reach agreement on the order of events, and to record them consistently and immutably without centralized trust. In some cases, however, the blockchain can benefit from some controlled mutability. Examples include removing private information or unlawful content, and correcting protocol vulnerabilities which would otherwise require a hard fork. Two approaches to control the mutability are: moderation, where one or more designated administrators can use their private keys to approve a redaction, and voting, where miners can vote to endorse a suggested redaction. In this paper, we first present several attacks against existing redactable blockchain solutions. Next, we provide a definitional framework for moderated redactable blockchains. Finally, we propose a provable and efficient construct, which applies a single digital signature per redaction, achieving a much simpler and secure result compared to the prior art in the moderated setting.