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arXiv 2106.079762021-06cs.LGcs.SYeess.SY

Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection

Viterbi School of Engineering（维特比工程学院）；University of Southern California（南加州大学）

Tuo Zhang, Chaoyang He, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr

发表信息：: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, November 2021, Pages 413-419
DOI：: 10.1145/3485730.3493444

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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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