Non-IID data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection
File(s)
Published version
Author(s)
Wang, Han
Muñoz-González, Luis
Eklund, David
Raza, Shahid
Type
Conference Paper
Abstract
The increase of the computational power in edge devices has enabled the penetration of distributed machine learning technologies such as federated learning, which allows to build collaborative models performing the training locally in the edge devices, improving the efficiency and the privacy for training of machine learning models, as the data remains in the edge devices. However, in some IoT networks the connectivity between devices and system components can be limited, which prevents the use of federated learning, as it requires a central node to orchestrate the training of the model. To sidestep this, peer-to-peer learning appears as a promising solution, as it does not require such an orchestrator. On the other side, the security challenges in IoT deployments have fostered the use of machine learning for attack and anomaly detection. In these problems, under supervised learning approaches, the training datasets are typically imbalanced, i.e. the number of anomalies is very small compared to the number of benign data points, which requires the use of re-balancing techniques to improve the algorithms' performance. In this paper, we propose a novel peer-to-peer algorithm,P2PK-SMOTE, to train supervised anomaly detection machine learning models in non-IID scenarios, including mechanisms to locally re-balance the training datasets via synthetic generation of data points from the minority class. To improve the performance in non-IID scenarios, we also include a mechanism for sharing a small fraction of synthetic data from the minority class across devices, aiming to reduce the risk of data de-identification. Our experimental evaluation in real datasets for IoT anomaly detection across a different set of scenarios validates the benefits of our proposed approach.
Date Issued
2021-06-28
Date Acceptance
2021-06-01
Citation
Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2021, pp.153-163
Publisher
ACM
Start Page
153
End Page
163
Journal / Book Title
Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Copyright Statement
© 2021 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License.
License URL
Identifier
https://dl.acm.org/doi/10.1145/3448300.3467827
Source
WiSec '21: 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Publication Status
Published
Start Date
2021-06-28
Finish Date
2021-07-02
Coverage Spatial
New York, NY, United States
Date Publish Online
2021-06-28