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An active federated method driven by inter-client informativeness variability of labeled data

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Abstract

Federated learning does well in jointly training multiple deep learning models for fault diagnosis. The accuracy of local model may affect the effectiveness of federated learning. In the case when the local model is suffered from few labeled data with poor quality, it is significant to design an active federation strategy to screen informative unlabeled samples by which supervised model can be further optimized. However, traditional active learning method for federated learning is unable to guarantee the effectiveness of federation since it focuses on screening the samples helpful for a specific local model rather than global model. This paper proposes a method of combining the variability of informativeness within the client and the variability between clients to overcome this problem. Informativeness variability of labeled data between clients is used to guide the screen strategy using active federation model rather than local model, such that influence of unreliable clients can be decreased to avoid negative transferring due to samples with low quality. Experimental validation for benchmark dataset of rolling bearing shows that 25.04% improvement of fault diagnosis accuracy can be achieved in the case when only few labeled data are available and 13.6% improvement can be achieved in the case when only labeled data with low quality are available.

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References

  1. Tian, J.H., Han, D.Y., Zhang, Y., et al.: Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis. Neural Netw. 162, 69–82 (2023)

    Article  Google Scholar 

  2. Yang, D.G., Karimi, H.R., Sun, K., et al.: Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples. Neural Netw. 141, 133–144 (2023)

    Article  Google Scholar 

  3. Tsai, N.C., King, Y.H., Lee, R.M.: Fault diagnosis for magnetic bearing systems. Mech. Syst. Signal Process. 23(4), 1339–1351 (2009)

    Article  Google Scholar 

  4. Chen, P., Zhao, X., Zhu, Q.: A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing. Appl. Intell. 50(9), 2833–2847 (2020)

    Article  Google Scholar 

  5. Peng, B., Xia, H., Lv, X., et al.: An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network. Appl. Intell. 52(3), 3051–3065 (2022)

    Article  Google Scholar 

  6. Qian, C.H., Zhu, J.J., Shen, Y.H., et al.: Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge. Neural Process. Lett. 54(3), 2509–2531 (2022)

    Article  Google Scholar 

  7. Tang, S.N., Yuan, S.Q., Zhu, Y.: Deep learning-based intelligent fault diagnosis methods toward rotating machinery. IEEE Access 8, 9335–9346 (2020)

    Article  Google Scholar 

  8. Duan, L.X., Xie, M.Y., Wang, J.J.: Deep learning enabled intelligent fault diagnosis: Overview and applications. J. Intell. Fuzzy Syst. 35(5), 5771–5784 (2018)

    Article  Google Scholar 

  9. Velayuthapandian, K., Subramoniam, S.P.: A focus module- based lightweight end-to-end CNN framework for voiceprint recognition. SIViP (2023)

  10. Priyadharshini, G., Ferni Ukrit, U.: CSO-CNN: Circulatory system optimization-based cascade region CNN for fault estimation and driver behavior detection. SIViP (2023).

  11. Park, S., Ahn, G.J., Im, D.H.: Auto labeling methods developed through semi-weakly supervised learning in prognostics and health management applications for rolling Ball Bearing. IEEE Sens. J. 22(16), 16223–16233 (2022)

    Article  Google Scholar 

  12. Tang, Y., Yang, G. Ding, D., et al.: Multi-level amplified iterative training of semi-supervision deep learning for glaucoma diagnosis. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 747–750 (2021)

  13. Zhang, Z., She, Z., Zhang, A.H.: A semi-supervision fault diagnosis method based on attitude information for a satellite. IEEE Access 5, 20303–20312 (2017)

    Article  Google Scholar 

  14. Ren, P.Z., Xiao, Y., Chang, X.J., et al.: A survey of deep active learning. ACM Comput. Surv. 54(9), 1 (2021)

    Article  Google Scholar 

  15. Chen, M., Zhu, K., Wang, R., et al.: Active learning-based fault diagnosis in self-organizing cellular networks. IEEE Commun. Lett. 24(8), 1734–1737 (2020)

    Article  Google Scholar 

  16. Jian, C.X., Yang, K.J., Ao, Y.H.: Industrial fault diagnosis based on active learning and semi-supervised learning using small training set. Eng. Appl. Artif. Intell. 104, 1 (2021)

    Article  Google Scholar 

  17. Huang, S.J., Zhao, J.W., Liu, Z.Y.: Cost-effective training of deep CNNs with active model adaptation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1580 –1588. Association for Computing Machinery, London (2018)

  18. Liu, Z., Zhang, J., He, X., et al.: Fault diagnosis of rotating machinery with limited expert interaction: A multicriteria active learning approach based on broad learning system. IEEE Trans. Control Syst. Technol. 31(2), 953–960 (2023)

    Article  Google Scholar 

  19. Sun, X.Y., Liu, D.H., Bian, JP.: The study of fault diagnosis model of DGA for oil-immersed transformer based on SVM active learning and K-L feature extracting. In: 2008 International Conference on Machine Learning and Cybernetics, pp. 1510–1514 (2018)

  20. Han, Z., Wang, J.: An adaptive active learning method for fault diagnosis. In: 2016 35th Chinese Control Conference (CCC), pp. 6421–6425 (2016)

  21. Shi, J., Wang, P., Li, H., et al.: Nonparametric active learning on bearing fault diagnosis. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 2230–2234 (2020)

  22. Lin, J., Ma, J., Zhu, J.: Hierarchical federated learning for power transformer fault diagnosis. IEEE Trans. Instrum. Measur. 71, 1–11 (2022)

    Google Scholar 

  23. McMahan, H.B., Moore, E., Ramage, D., et al.: Communication-efficient learning of deep networks from decentralized data. Artif. Intell. Stat. 2017, 1273–1282 (2017)

    Google Scholar 

  24. Li, Y., Chen, Y., Zhu, K., et al.: An effective federated learning verification strategy and its applications for fault diagnosis in industrial IoT systems. IEEE Internet Things J. 9(18), 16835–16849 (2022)

    Article  Google Scholar 

  25. Zhang, Z., Xu, X., Gong, W., et al.: Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis. Control Eng. Practice 116, 104913 (2021)

    Article  Google Scholar 

  26. Chen, J., Tang, J., Chen, J.: Federated meta-learning framework for few-shot fault diagnosis in industrial IoT. In: GLOBECOM 2022–2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, pp. 2993–2998 (2022)

  27. Chen, J., Li, J., Huang, R.: Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging. IEEE Trans. Instrum. Measur. 71, 1–11 (2022)

    Google Scholar 

  28. Chen, J., Li, J., Huang, R., et al.: Federated learning for bearing fault diagnosis with dynamic weighted averaging. In: 2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), pp. 1–6 (2021)

  29. Ma, X., Wen, C., Wen, T.: An asynchronous and real-time update paradigm of federated learning for fault diagnosis. IEEE Trans. Ind. Inf. 17(12), 8531–8540 (2021)

    Article  Google Scholar 

  30. Zhang, Z., Guan, C., Chen, H., et al.: Adaptive privacy-preserving federated learning for fault diagnosis in internet of ships. IEEE Internet Things J. 9(9), 6844–6854 (2022)

    Article  Google Scholar 

  31. Ahmed, L., Ahmad, K., Said, N., et al.: Active learning based federated learning for waste and natural disaster image classification. IEEE Access 8, 208518–208531 (2020)

    Article  Google Scholar 

  32. Bearing data Centre: Case Western Reserve University. http://cse-groups.case.edu/bearingdatacenter/home.

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Funding

This work was supported by the National Natural Science Foundation of China (62073213) and the National Natural Science Foundation of China Youth Fund (52205111). Supported by the Opening Project of Guangdong Provincial Key Lab of Robotics and Intelligent System (518055)

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Authors and Affiliations

  1. School of Logistic Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Funa Zhou, Chang Wang, Xiong Hu, Chaoge Wang & Tianzhen Wang

Authors
  1. Funa Zhou
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  2. Chang Wang
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  3. Xiong Hu
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  4. Chaoge Wang
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  5. Tianzhen Wang
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Contributions

CW contributed to method design, validation, writing—original draft, writing—review and editing. FZ contributed to formal analysis, investigation, resources, and writing—review and editing. CW contributed to methodology. XH contributed to resources. FZ and CW wrote the main manuscript text and, XH, TW provide revision and suggestions. All authors reviewed manuscript.

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Correspondence to Chang Wang.

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Zhou, F., Wang, C., Hu, X. et al. An active federated method driven by inter-client informativeness variability of labeled data. SIViP 17, 3973–3982 (2023). https://doi.org/10.1007/s11760-023-02627-7

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  • DOI: https://doi.org/10.1007/s11760-023-02627-7

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