sesa 18: e4

Research Article

FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning

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  • @ARTICLE{10.4108/eai.21-10-2021.171595,
        author={Zezhang Yang and Jian Li and Ping Yang},
        title={FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning},
        journal={EAI Endorsed Transactions on Security and Safety: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SESA},
        year={2021},
        month={10},
        keywords={Federated learning, Anomaly detection, Mobility prediction, Privacy-preserving system},
        doi={10.4108/eai.21-10-2021.171595}
    }
    
  • Zezhang Yang
    Jian Li
    Ping Yang
    Year: 2021
    FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning
    SESA
    EAI
    DOI: 10.4108/eai.21-10-2021.171595
Zezhang Yang1, Jian Li1,*, Ping Yang2
  • 1: Department of Electrical and Computer Engineering, Binghamton University, State University of New York, Binghamton, NY 13902
  • 2: Department of Computer Science, Binghamton University, State University of New York, Binghamton, NY 13902
*Contact email: lij@binghamton.edu

Abstract

With the proliferation of mobile devices and smart cameras, detecting anomalies and predicting their mobility are critical for enhancing safety in ubiquitous computing systems. Due to data privacy regulations and limited communication bandwidth, it is infeasible to collect, transmit, and store all data from mobile devices at a central location. To overcome this challenge, we propose FedADMP, a federated learning based joint Anomaly Detection and Mobility Prediction framework. FedADMP adaptively splits the training process between the server and clients to reduce computation loads on clients. To protect the privacy of user data, clients in FedADMP upload only intermediate model parameters to the cloud server. We also develop a differential privacy method to prevent the cloud server and external attackers from inferring private information during the model upload procedure. Extensive experiments using real-world datasets show that FedADMP consistently outperforms existing methods.