Abstract
In recent years, machine learning technology has been widely used in many fields, such as smart transportation, smart healthcare, smart finance and smart cities. Although machine learning technology has brought people a lot of convenience, the privacy problem of user data has also emerged [1]. Considering that users are not necessarily willing to upload personal privacy data to the cloud for deep learning training, therefore, instead of consuming a lot of bandwidth to upload data to the cloud, it is better to train on the local device and then use the model parameters obtained after training. (For example: weights and bias, etc.) upload to the server for aggregation. This emerging machine learning technology is called federated learning. In this way, the privacy and security of data can be guaranteed, and the purpose of decentralized learning can be achieved through aggregation. This study uses the architecture of federated learning technology and convolutional neural network algorithms to implement distributed image recognition mobile applications. This application allows users to use their mobile devices and the central servers for repeated training. After multiple rounds of repeated training, the convergence will be stabilized, and the accuracy will be significantly improved. At the same time, it can take into account privacy and achieve the machine the purpose of learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mohassel, P., Zhang, Y.: Secureml: A system for scalable privacy-preserving machine learning. Proc. of the 2017 IEEE symposium on security and privacy (May 2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553) (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proc. of the IEEE conference on computer vision and pattern recognition (CVPR) (June 2016)
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1), pp. 172–186 (1 Jan. 2021)
Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., Stolcke, A.: The Microsoft 2017 conversational speech recognition system. In: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (April 2018)
Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)
Qian, M., Fei, H., Hao, Q.: Deep learning for intelligent wireless networks: a comprehensive survey. IEEE Communications Surveys & Tutorials 20(4), 2595–2621 (2018)
Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency (2016). arXiv preprint arXiv:1610.05492
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Sys. Technol. (TIST) 10(2), 1–19 (2019)
Bonawitz, K., et al.: Towards federated learning at scale: System design (2019). arXiv preprint arXiv:1902.01046
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: Proc. of 2017 International Conference on Engineering and Technology (ICET) (Aug. 2017)
The CIFAR-10 dataset https://www.cs.toronto.edu/~kriz/cifar.html
Visualization - Deeplearning4j, https://deeplearning4j.konduit.ai/tuning-and-training/visualization
Acknowledgement
This work was supported in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 110-2621-M-029-003-, 110-2221-E-029-002-MY3, 110-2221-E-126-004- and 110-2622-E-029-003-.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Chan, YW., Wu, BY., Huang, YM., Yang, CT. (2022). Design and Implementation of Distributed Image Recognition App with Federal Learning Techniques. In: Lin, YB., Deng, DJ., Yang, CT. (eds) Smart Grid and Internet of Things. SGIoT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-031-20398-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-20398-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20397-8
Online ISBN: 978-3-031-20398-5
eBook Packages: Computer ScienceComputer Science (R0)