Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection (2106.07976v4)

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 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.

• The paper presents FedIoT with the innovative FedDetect algorithm for unsupervised deep anomaly detection on IoT devices, achieving 98.27% accuracy.
• It employs adaptive optimization techniques including Adam and a cross-round learning rate scheduler to refine decentralized on-device training while preserving data privacy.
• Experimental evaluations on the N-BaIoT dataset confirm enhanced detection performance and efficient resource utilization, promoting scalable edge-device security.

Insights into Federated Learning for IoT Anomaly Detection

The paper "Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection" discusses the development of a federated learning framework aimed at enhancing cybersecurity in IoT environments while safeguarding data privacy. The advanced federated learning platform, FedIoT, introduced in this paper, focuses on anomaly detection in IoT devices using the FedDetect algorithm. Notable improvements are demonstrated in performance and system efficiency, particularly through the integration of an adaptive optimizer and cross-round learning rate scheduler.

Objective and Execution

The paper underscores the challenges posed by centralized data management approaches, especially in IoT settings characterized by high data transmission frequencies and privacy concerns. To combat these challenges, the FedIoT platform is equipped to work across realistic IoT devices, utilizing federated learning to build global models without requiring data centralization.

Methodology

FedDetect, the algorithm emphasized in the paper, adopts a deep Autoencoder model, recognized for its capability in unsupervised anomaly detection. The framework deviates from the conventional FedAvg by incorporating adaptive optimization techniques such as Adam and adopting a unique cross-round learning rate scheduler to optimize local training. This approach enhances federated learning adaptation across diverse edge devices like Raspberry Pis.

Experimental Evaluation

The evaluation of FedIoT, employing the N-BaIoT dataset, showcases the framework's ability to detect a wide variety of attack types within IoT networks. The results reveal that FedIoT, with its distributed training protocol, achieves detection accuracy (98.27%) comparable to centralized techniques, albeit with a marginal trade-off in false positive rates (3.45%). The system performance extends beyond mere detection efficacy, revealing that memory and computational costs remain well within feasible limits for edge devices, with a training time of less than an hour.

Algorithmic and System Implications

The proposed FedDetect algorithm represents a significant advancement in federated anomaly detection, providing a generalized global threshold for consistent anomaly assessment across IoT networks. System design improvements advocate for lightweight, modular communication strategies via MQTT and adaptable infrastructure capable of scaling across IoT edge devices.

Future Directions

This research opens avenues for further exploration in federated learning applications within resource-constrained environments, prompting additional advancements in algorithmic efficiency and data security. Future iterations may emphasize reducing communication overheads further through advanced compression techniques and enhancing distributed training scalability. As IoT device proliferation continues to escalate with the expansion of 5G technologies, frameworks like FedIoT hold the potential to redefine edge-device security protocols.

In summary, the FedIoT platform and FedDetect algorithm represent impactful contributions to federated learning by demonstrating that effective, privacy-preserving anomaly detection in IoT devices can be achieved without centralized data processing. This research serves as a foundation for future developments in distributed learning systems and invites ongoing innovation in adaptive federated methodologies and system designs.