Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
This work addresses IoT cybersecurity for resource-constrained devices, but it is incremental as it builds on existing federated learning methods.
The paper tackles on-device anomaly detection for IoT cybersecurity by proposing the FedIoT platform and FedDetect algorithm, which improve performance using a local adaptive optimizer and cross-round learning rate scheduler, with results showing efficacy in detecting a wider range of attack types and affordable training time and memory cost for resource-constrained devices.
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.