Federated Learning for Internet of Things
Open accessAbstract
Federated learning can be a promising solution for enabling IoT cybersecurity (i.e., anomaly detection in the IoT environment) while preserving data privacy and mitigating the high communication/storage overhead (e.g., high-frequency data from time-series sensors) of centralized over-the-cloud approaches. In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices. Furthermore, the proposed FedDetect learning framework improves the performance by utilizing a local adaptive optimizer (e.g., Adam) and a cross-round learning rate scheduler. In a network of realistic IoT devices (Raspberry PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance. Our results demonstrate the efficacy of federated learning in detecting a wider range of attack types occurred at multiple devices. The system efficiency analysis indicates that both end-to-end training time and memory cost are affordable and promising for resource-constrained IoT devices. The source code is publicly available at https://github.com/FedML-AI/FedIoT.
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FedIoT [15] provides a mature systems-level framework that developers can use to deploy federated learning applications on CPU or GPU-enabled IoT devices, such as Raspberry Pi and NVIDIA Jetson Nano.
“One work of note along this direction is FedIoT [15] which provides a mature systems-level framework that the developer can use to deploy their federated learning applications on CPU or GPU-enabled IoT devices, such as Raspberry Pi and NVIDIA Jetson Nano.”
From Federated learning for the Internet of Things: Applications, challenges, and opportunities (2022) · cites this paper as [15]
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References 20 · Cited by 157
Research works printed but not linked: 2
- Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019). · arXiv
- H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas. 2016. Federated Learning of Deep Networks using Model Averaging. CoRR abs/1602.05629 (2016). arXiv:1602.05629 http://arxiv.org/abs/1602.05629 · arXiv
- Authors
- Zhang, T., He, C., Ma, T., Gao, L., Ma, M., & Avestimehr, S.
- Affiliations
- University of Southern California (US)
- Venue
- Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
- Publisher
- ACM
- Published
- 2021-11-15
- Pages
- 413-419
- OpenAlex
- W3212135426
- Cited by
- 157
- References
- 20
- Citations by year
- 2026: 21 · 2025: 46 · 2024: 36 · 2023: 38 · 2022: 12 · 2021: 4
- FWCI
- 10.39
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Embedded Networked Sensor Systems (SenSys) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- NSF (National Science Foundation) (N00014-16-1-2189)
- Cisco Systems
- Defense Advanced Research Projects Agency (W911NF1810400)
- Konica Minolta Imaging Science Foundation
- PrivateAI institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- federated learning, Internet of Things, Internet of Things security, anomaly detection, on-device learning, data privacy, adaptive optimization algorithm, learning rate scheduling, resource-constrained devices, Raspberry Pi, attack detection
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