FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning
Keywords:Federated learning, Anomaly detection, Mobility prediction, Privacy-preserving system
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.
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Copyright (c) 2022 EAI Endorsed Transactions on Security and Safety
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Office of Energy Efficiency and Renewable Energy
Grant numbers DE-EE0009341;OAC- 1738929