Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data

Mohamad Arafeh, Mohamad Wazzeh, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Hadi Otrok

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Scopus citations

    Abstract

    This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation.

    Original languageBritish English
    Title of host publication2023 7th Cyber Security in Networking Conference, CSNet 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages143-147
    Number of pages5
    ISBN (Electronic)9798350342871
    DOIs
    StatePublished - 2023
    Event7th Cyber Security in Networking Conference, CSNet 2023 - Montreal, Canada
    Duration: 16 Oct 202318 Oct 2023

    Publication series

    Name2023 7th Cyber Security in Networking Conference, CSNet 2023

    Conference

    Conference7th Cyber Security in Networking Conference, CSNet 2023
    Country/TerritoryCanada
    CityMontreal
    Period16/10/2318/10/23

    Keywords

    • Clustering
    • Federated Learning
    • Machine Learning
    • Non-IID
    • Participants Selection
    • Privacy
    • Split Learning

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