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FedProLs: federated learning for IoT perception data prediction

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Abstract

With the development of Internet of Things, sensor devices collect massive amounts of data. However, due to privacy protection requirement, data cannot be shared and collected. How to integrate independent perception data into deep learning is one of the most challenging problems. In this paper, we present a novel framework (FedProLs) for IoT perception data prediction based on a horizontal federated learning model. The framework is constructed by the client nodes and the server nodes, and the training data of the federated learning system is deployed on the client nodes. Each client uses its own data to train machine learning models locally and encrypts its training model parameters and sends it to the server nodes. The server node uses the federated averaging method to construct a global model for prediction. In addition, we propose a new multi-feature factor model (ProLs) as a client-node machine learning model. Finally, the proposed FedProLs and ProLs models are compared with the single model Prophet, LSTM and BP Neural Networks, and combine model CNN-LSTM, ARIMA. The experimental results using two real-life IoT perception data sets demonstrate that the FedProLs and the participants’ ProLs achieves better results in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) than existing methods. The FedProLs model is suitable for distributed independent data protection when predicting the perception data of Internet of Things (IOT).

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References

  1. Hao M., Li H., Luo X., Xu G., Yang H., Liu S. (2020) Efficient and Privacy-Enhanced federated learning for industrial artificial intelligence. IEEE Trans Ind Inform 16(10):6532–6542

    Article  Google Scholar 

  2. Sun X., Gui G., Li Y., Liu R.P., An Y. (2019) Resinnet: A Novel Deep Neural Network With Feature Reuse for Internet of Things. IEEE Internet Things J 6(1):679–691

    Article  Google Scholar 

  3. Kawamoto Y., Yamada N., Nishiyama H., Kato N., Shimizu Y., Zheng Y. (2017) A Feedback Control-Based Crowd Dynamics Management in IoT System. IEEE Internet Things J 4(5):1466–1476

    Article  Google Scholar 

  4. Ni J., Lin X., Shen X.S. (2018) Efficient and Secure Service-Oriented Authentication Supporting NetworkSlicing for 5G-Enabled IoT. IEEE J Sel Areas Commun 36(3):644–657

    Article  Google Scholar 

  5. Ni J., Zhang K., Lin X., Shen X. (2018) Securing Fog Computing for Internet of Things applications: Challenges and Solutions. IEEE Commun Surv Tut 20(1):601–628

    Article  Google Scholar 

  6. Lu Y., Huang X., Dai Y., Maharjan S., Zhang Y. (2020) Blockchain and Federated Learning for Privacy-Preserved Data Sharing in IndustrialIoT. IEEE Trans Ind Inform 16(6):4177–4186

    Article  Google Scholar 

  7. Jindal A., Aujla G. S., Kumar N., Prodan R., Obaidat M. S. (2018) DRUMS: Demand response management in a smart city using deep learning and SVR

  8. Li W. S., Wang L., Chen C. (2018) Application and design of LSTM in coal mine gas prediction and warning system. Journal of Xian University of Science and Technology 38(6):1027–1035

    Google Scholar 

  9. Wu T., Liu C., He C. (2020) Prediction of Egional Temperature Change Trend Based on LSTM Algorithm. In: Proc. IEEE (ITNEC), Chongqing, China, pp 62–66

  10. Yang Q., Liu Y., Cheng Y., Kang Y., Chen T.J., Yu H. (2020) Federated learning. Publishing House of Electronics Industry, pp 54–67

  11. McMahan H. B., Moore E., Ramage D., Hampson S. (2017) Communication-efficient learning of deep networks from decentralized data. In: Proc. Conf Machine Learning Research, Fort Lauderdale, FL USA

  12. Yang Q., Liu Y., Chen T. J., Tong Y. (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst technol 10(2):1C15

    Article  Google Scholar 

  13. Cheng K., Fan T., Jin Y., Liu Y., Chen T.J., Yang Q. (2019) SecureBoost: A lossless federated learning framework, [Online]. Available: https://arxiv.org/pdf/1901.08755.pdf

  14. Liu Y., Chen T., Yang Q. (2018) Secure Federated Transfer Learning, [Online]. Available: https://arxiv.org/pdf/1812.03337.pdf

  15. Lim H-K, Kim J-B, Heo J-S, Han Y-H (2020) Federated reinforcement learning for training control policies on multiple IoT devices. Sensors 20(5):1359

    Article  Google Scholar 

  16. Smith V., Chiang C. K., Sanjabi M., Talwalkar A. S. (2017) Federated multi-task learning. In: Proc. Advances in Neural Information Processing Systems, Long Beach, CA USA

  17. Sheller M.J., Reina G.A., Edwards B., Martin J., Bakas S. (2019) Multiinstitutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In: Proc. Int. MICCAI Brainlesion Workshop, in Lecture Notes in Computer Science, Cham, Switzerland: Springer, (11383):92C104

  18. Chen M., Mathews R., Ouyang T., Beaufays F. (2019) Federated learning of out-of-vocabulary words, [Online]. Available: https://arxiv.org/pdf/1903.10635.pdf

  19. Ammad-Ud-Din M., Ivannikova E., Khan S.A., Oyomno W., Fu Q., Tan K.E., Flanagan A. (2019) Federated collaborative filtering for privacy-preserving personalized recommendation system, [Online]. Available: https://arxiv.org/pdf/1901.09888.pdf

  20. Ding Z., Gao X., Xu J., Wu H. (2013) IOT-StatisticDB: A General Statistical Database Cluster Mechanism for Big Data Analysis in the Internet of Things, in proc. IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, Beijing, 535–543

  21. He K, Wang Z, Li D, Zhu F, Fan L (2020) Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT. Physical Communication 43:101181. ISSN 1874-4907, https://doi.org/10.1016/j.phycom.2020.101181

    Article  Google Scholar 

  22. Xia J., Deng D., Fan D. (2020) A Note on Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters. IEEE Transactions on Broadcasting 66(3):744–745. https://doi.org/10.1109/TBC.2020.2985592https://doi.org/10.1109/TBC.2020.2985592

    Article  Google Scholar 

  23. Lee M., Hwang J., Yoe H. (2013) Agricultural Production System Based on IoT. In: Proc International Conference on Computational Science and Engineering, Sydney, NSW, IEEE 16th, pp 833–837

  24. Chen Y., Zhang N., Zhang Y., Chen X. (2019) Dynamic Computation Offloading in Edge Computing for Internet of Things. IEEE Internet Things J 6(3):4242–4251

    Article  Google Scholar 

  25. Mao Y., Yang F., Wang C. (2011) Application of BP network to short-term power load forecasting considering weather factor, in Proc International Conference on Electric Information and Control Engineering, Wuhan, pp 172–175

  26. Guo R., Xu G.L. (2013) Research on multi-sensor prediction model of coal mine gas concentration based on information fusion and GA-SVM. Chinese Journal of Safety Science 23(9):33–38

    Google Scholar 

  27. Akpinar M., Yumusak N. (2013) Forecasting household natural gas consumption with ARIMA model: A case study of removing cycle. In: Proc International Conference on Application of Information and Communication Technologies, Baku, pp 1–6

  28. Farrugia R.A. (2012) Improving motion vector prediction using linear regression, 5th International Symposium on Communications, Control and Signal Processing, Rome, pp 1–4

  29. Yang H. (2017) Research on weather forecasting based on deep learning, M.S. thesis, Dept, Harbin Institute of Technology, China

  30. Zhao Y. X., Yang Z. L., Ma B. J., Song H. H., Yang D. H. (2020) Deep learning prediction and model generalization of ground pressure for deep longwall face with large mining height. Journal of China Coal Society 45(1):54–65

    Google Scholar 

  31. Yang Y. J., Wang D. C., Chen S. J., et al. (2010) AE Predicting study on compression and fracture of limestone sample based on discrete wavelet analysis. Journal of China Coal Society 35(2):213– 217

    Google Scholar 

  32. Taylor S.J., Letham B. (2019) Forecasting at scale, [Online]. Available: https://facebookincubator.github.io/prophet/static/prophet_paper_20170113.pdf

  33. Gong F., Han N., Li D., Tian S. (2020) Trend Analysis of Building Power Consumption Based on Prophet Algorithm, 2020 Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, pp 1002–1006

  34. Li L. P., Duan G. H., Wang J. X. (2019) Reserve prediction of bank outlets based on prophet framework. Journal of Central South University (Science and Technology) 50(1):75–82

    Google Scholar 

  35. Hochreiter B., Schmidhuber J. (1997) Long short term memory. Neural Comput 9:1735–1780

    Article  Google Scholar 

  36. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. [Online]. Available: https://arxiv.org/pdf/1506.00019.pdf

  37. Livieris IE, Pintelas E, Pintelas P (2020) A CNNCLSTM model for gold price time-series forecasting. Neural Comput and Applic 32:17351C17360

    Article  Google Scholar 

  38. Box GEP, Jenkins GM (2010) Time series analysis : forecasting and control. Journal of Time, 31(3)

  39. Shuwen J, Tingting Y (2021) Research on Stock Price Forecasting Based on BP Neural Network. Advances in Artificial Intelligence and Security, pp 663–673

  40. Yu A, Lai WL, Payor J (2015) Efficient Integer Vector Homomorphic Encryption. http://courses.csail.mit.edu/6.857/2015/files/yu-lai-payor.pdf

Download references

Acknowledgments

This work was supported in part by NSFC (U1931207 and 61702306), Sci. & Tech. Development Fund of Shandong Province of China (2016ZDJS02A11, ZR2017BF015 and ZR2017MF027), the Taishan Scholar Program of Shandong Province(No. ts20190936), SDUST Research Fund (2015TDJH102 and 2019KJN024), and Shandong Chongqing Science and Technology Cooperation Project (cstc2020jscx-lyjsAX0008).

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Authors and Affiliations

  1. Shandong University of Science and Technology, Qingdao, China

    Qingtian Zeng, Zhenzhen Lv, Chao Li, Yongkui Shi, Zedong Lin, Cong Liu & Ge Song

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  1. Qingtian Zeng
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  3. Chao Li
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Correspondence to Chao Li or Ge Song.

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Zeng, Q., Lv, Z., Li, C. et al. FedProLs: federated learning for IoT perception data prediction. Appl Intell 53, 3563–3575 (2023). https://doi.org/10.1007/s10489-022-03578-1

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