Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Anomaly Detection for Time-series Data in Industrial IoT: A
  Communication-Efficient On-device Federated Learning Approach   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.

33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th