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.
翻译:联邦学习可以是一个大有希望的解决办法,既可以使IoT网络安全(即在IoT环境中发现异常现象),同时又可以保护数据隐私,减少中央超频传感器的高通信/存储管理费用(例如时间序列传感器的高频数据),在本文中,通过对算法和系统设计进行综合研究,进一步推进这一方向,我们建立了FedIoT平台,该平台包含FedSet检测异常现象数据实时检测算法和系统设计,用于对IoT设备联合学习进行现实评估。 此外,拟议的FedSet检测学习框架利用当地适应性优化器(例如Adam)和跨轮学习率计时器改进了业绩。在一个现实的IoT装置网络(Raspberry PI)中,我们评估FedIot平台和FedSta检测算法在模型和系统性能两方面的功能。我们的结果显示,Federate学习在发现多种攻击类型袭击时的效率。系统效率分析表明,终端培训时间和记忆-Faminal-Fab-Fared 用于资源源码/MLATRADrtraed/FADral-ADraldrodrods