• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Time-dependent and Privacy-Preserving Decentralized Routing using Federated Learning

    Samal, Chinmaya
    : https://etd.library.vanderbilt.edu/etd-07262019-213445
    http://hdl.handle.net/1803/13627
    : 2019-07-30

    Abstract

    With rapid urbanization, route planning is gaining more importance. As transportation networks become more complex and mobility in our society more important, the demand for efficient methods in route planning increases even further. State of the art solutions for route planning in a time-dependent network assumes a centralized approach, where parallelization of the search algorithm uses a shared memory model. Hence, its deployment is limited to multiprocessing environment such as in a data center, where it is assumed that a shared memory allows a constant time direct communication between each pair of processors. It is not well suited for a distributed setting, which is prone to communication failures and can incur higher response times due to network latency. Furthermore, storing and using location data of users raises privacy concerns. This thesis describes a resilient, decentralized approach for route planning in a time-dependent network, where the computing devices have limited resources and operate in an environment with intermittent network connectivity. We leverage recent advances in federated learning to collaboratively learn shared prediction models online while keeping all the training data on the device, thus preserving privacy. As everyday devices are becoming more powerful, our approach can effectively tackle the urban routing problem by harnessing the device resources. Our approach will particularly help cities with a limited budget and network coverage, provide self-sustaining mobility services for its residents while still preserving their privacy. We show the effectiveness of our approach and provide analysis using a case study from the Metropolitan Nashville area.
    Show full item record

    Files in this item

    Icon
    Name:
    Samal.pdf
    Size:
    1.512Mb
    Format:
    PDF
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy