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

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.

References in corpus (8)

• Adam: A Method for Stochastic Optimization
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders
• Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
• Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
• Communication Compression for Decentralized Training
• Local Adaptivity in Federated Learning: Convergence and Consistency
• An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices

Cited by in corpus (8)

• From Distributed Machine Learning to Federated Learning: A Survey
• The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
• CADeSH: Collaborative Anomaly Detection for Smart Homes
• A Simple Python Testbed for Federated Learning Algorithms
• MicroPython Testbed for Federated Learning Algorithms
• A Federated Learning Algorithms Development Paradigm
• An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
• FedOptima: Optimizing Resource Utilization in Federated Learning