Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach (2007.09712v1)

Abstract: Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user's private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50\% compared to the federated learning framework that does not use a gradient compression scheme.

• The paper introduces a decentralized federated learning framework that preserves data privacy while detecting anomalies in distributed IIoT environments.
• The paper employs an AMCNN-LSTM model that leverages attention mechanisms to enhance feature extraction and time-series prediction accuracy.
• The paper demonstrates that gradient compression reduces communication overhead by approximately 50%, enabling efficient convergence across real-world datasets.

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

The paper explores a methodical approach to anomaly detection in Industrial Internet of Things (IIoT) environments, which is both privacy-conscious and efficient in terms of communication. This is achieved through Federated Learning (FL), a framework that allows edge devices to collaboratively train models without exposing raw data, thereby preserving privacy.

Key Contributions

1. Federated Learning Framework: The paper introduces a decentralized FL framework that trains a global anomaly detection model using edge devices. This addresses the privacy concerns and "data islands" issue typically encountered in anomaly detection tasks across distributed systems.
2. AMCNN-LSTM Model: The anomaly detection methodology employs an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model. This model leverages attention mechanisms within Convolutional Neural Networks (CNNs) to focus on critical features, while LSTMs deal with time-series predictions, maximizing both feature extraction and temporal data prediction capabilities.
3. Communication Efficiency via Gradient Compression: To alleviate the communication overhead, a gradient compression mechanism utilizing Top-k selection is applied. This method compresses uploaded gradients, crucial in federated scenarios to reduce communication burdens and hasten convergence without sacrificing model performance.

Technical Evaluation

The AMCNN-LSTM model improves the generalization of anomaly detection, resolving issues such as memory loss and gradient dispersion. Experiments on four real-world datasets show that this framework reduces communication overhead by approximately 50% when compared to traditional federated learning setups that do not implement gradient compression.

In terms of accuracy, the proposed setup demonstrates superior performance across datasets (power demand, space shuttle, ECG, and engine), capitalizing on its robust architecture to detect irregularities effectively. Key to this success is the integration of CNNs and LSTMs with attention mechanisms, which refine feature scrutiny and maintain model scalability within the federated learning process.

Implications and Future Developments

The implications of this research are substantial for real-time anomaly detection in IIoT environments, where timely responses to edge device failures can prevent significant production disruptions and financial losses. The approach maintains data privacy—an ever-growing concern—inherent to edge computing's decentralized nature.

Looking forward, further enhancements could be considered. The introduction of more complex models could refine anomaly detection, though this comes with computational trade-offs. Future work might explore adaptive FL frameworks that dynamically balance model complexity and communication constraints based on the operational environment's demands. Additionally, the gradient compression approach could be further optimized to manage higher device heterogeneity or variable communication bandwidths in edge networks.

Overall, this research provides a compelling framework for effective and privacy-preserving anomaly detection within the burgeoning domain of industrial IoT, setting a precedent for future advancements in distributed learning and anomaly detection technologies.