The rise of privacy concerns in face recognition applications necessitates the development of secure machine learning frameworks. This paper introduces Differentially Private Asynchronous Federated Learning with Buffered Aggregation (DP-AFLBA), a novel approach that combines differential privacy, asynchronous federated learning, and buffered aggregation to protect sensitive data while improving model accuracy. In DP-AFLBA, local model updates from participants are perturbed with carefully calibrated noise to ensure privacy, adhering to (ϵ, δ)-differential privacy standards. Asynchronous communication allows participants to send updates independently, reducing delays associated with traditional synchronous methods. To address the challenges of stale updates, a buffering mechanism aggregates updates over a defined time window, enabling the server to apply a more stable and accurate global model update. Experimental results demonstrate that DP-AFLBA achieves superior performance in terms of model accuracy and privacy protection compared to existing federated learning frameworks, making it particularly suitable for large-scale face recognition tasks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Differentially Private Asynchronous Federated Learning with Buffered Aggregation for Face Recognition


    Contributors:
    Xing, Tongkun (author) / Zhang, Ming (author)


    Publication date :

    2024-10-23


    Size :

    633520 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Secure Intrusion Detection by Differentially Private Federated Learning for Inter-Vehicle Networks

    Xu, Qian / Zhang, Lei / Ou, Dongxiu et al. | Transportation Research Record | 2023


    Cluster-Wise Response Aggregation-Based Differentially Private Pool Energy Market Model

    Dash, Shitikantha / Sodhi, Ranjana / Singh, Sri Niwas | IEEE | 2024


    Privacy-Preserving Resource Allocation for Asynchronous Federated Learning

    Chen, Xiaojing / Zhou, Zheer / Ni, Wei et al. | IEEE | 2024


    Asynchronous Federated Learning for Edge-assisted Vehicular Networks

    Wang, Siyuan / Wu, Qiong / Fan, Qiang et al. | ArXiv | 2022

    Free access

    On Vehicular Data Aggregation in Federated Learning

    Levente Alekszejenkó / Tadeusz Dobrowiecki | DOAJ | 2024

    Free access