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

• URL: http://arxiv.org/abs/2106.07976v1
• Date: Tue, 15 Jun 2021 08:53:42 GMT
• Title: Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
• Authors: Tuo Zhang, Chaoyang He, Tianhao Ma, Mark Ma, Salman Avestimehr
• Abstract summary: We build a FedIoT platform that contains a synthesized dataset using N-BaIoT, FedDetect algorithm, and a system design for IoT devices. In a network of realistic IoT devices (PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance.
• Score: 10.232121085973782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 a synthesized dataset using N-BaIoT, FedDetect algorithm, and a system design for IoT devices. Furthermore, the proposed FedDetect learning framework improves the performance by utilizing an 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 large range of attack types. 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.

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