Curriculum-Based Federated Learning for Machine Fault Diagnosis With Noisy Labels

Federated learning (FL) has emerged as an effective machine-learning paradigm for collaborative machine fault diagnosis in a privacy-preserving scheme. However, due to the perception limitation and different annotation criteria of annotators, the data in clients may have noisy labels with varied noi...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 20; no. 12; pp. 13820 - 13830
Main Authors: Sun, Wenjun, Yan, Ruqiang, Jin, Ruibing, Zhao, Rui, Chen, Zhenghua
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Annotations
Clients
Curricula
Curriculum development
Curriculum learning
Data models
Effectiveness
Fault diagnosis
Federated learning
federated learning (FL)
label noise
Labels
loss correction
Machine learning
Noise level
Noise levels
Noise measurement
Robustness
Training
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:Federated learning (FL) has emerged as an effective machine-learning paradigm for collaborative machine fault diagnosis in a privacy-preserving scheme. However, due to the perception limitation and different annotation criteria of annotators, the data in clients may have noisy labels with varied noise levels, leading to degraded FL performances. Most existing methods in FL for tackling the label noise issue, assume that there is label noise in all clients and treat all clients with the same denoising training. However, these methods may result in sub-optimization and even training instability of local models, so that they cannot perform well on heterogeneous label noise across clients in FL. To address this issue, we propose a curriculum-based federated learning (called FedCNL) method to combat the heterogeneous label noise in FL settings. First, our proposed FedCNL exploits a noise modeling module to adaptively estimate the clean clients and noisy clients, and identify the clean samples and noisy samples in noisy clients in an unsupervised manner. Then, a multi-stage curriculum learning is designed by regarding the noise level as learning complexity, where the model learns from clean to noisy samples, gradually improving the performance of the global model. Moreover, a mixed loss correction method is explored in the curriculum stage to maximize the utilization of data with noisy labels. Experiments performed on fault datasets in non-identically and independently distributed settings indicate that our proposed method addresses the label noise issue for machine fault diagnosis in heterogeneous FL with favorable effectiveness, achieving state-of-the-art performances.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3435449