FedADMP: A Joint Anomaly Detection and Mobility Prediction Framework via Federated Learning
- State Univ. of New York (SUNY), Binghamton, NY (United States); SUNY-Binghamton University
- State Univ. of New York (SUNY), Binghamton, NY (United States)
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.
- Research Organization:
- State Univ. of New York (SUNY), Binghamton, NY (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; National Science Foundation (NSF); Research Foundation of State University of New York
- Grant/Contract Number:
- EE0009341
- OSTI ID:
- 1835237
- Journal Information:
- ICST Transaction on Security and Safety, Journal Name: ICST Transaction on Security and Safety Journal Issue: 29 Vol. 8; ISSN 2032-9393
- Publisher:
- European Alliance for Innovation (EAI)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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