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http://dx.doi.org/10.7472/jksii.2022.23.3.97

The Study on the Implementation Approach of MLOps on Federated Learning System  

Hong, Seung-hoo (Dept. of Computer Engineering, Gachon University)
Lee, KangYoon (Dept. of Computer Engineering, Gachon University)
Publication Information
Journal of Internet Computing and Services / v.23, no.3, 2022 , pp. 97-110 More about this Journal
Abstract
Federated learning is a learning method capable of performing model learning without transmitting learning data. The IoT or healthcare field is sensitive to information leakage as it deals with users' personal information, so a lot of attention should be paid to system design, but when using federated-learning, data does not move from devices where data is collected. Accordingly, many federated-learning implementations have been developed, but detailed research on system design for the development and operation of systems using federated learning is insufficient. This study shows that measures for the life cycle, code version management, model serving, and device monitoring of federated learning are needed to be applied to actual projects and distributed to IoT devices, and we propose a design for a development environment that complements these points. The system proposed in this paper considered uninterrupted model-serving and includes source code and model version management, device state monitoring, and server-client learning schedule management.
Keywords
federated-learning; framework; open source; effective development environment; MLOps;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Pandey, V. and Bengani S., "Operationalizing Machine Learning Pipelines: Building Reusable and Reproducible Machine Learning Pipelines Using MLOps", Bpb Publications, (English Edition), 2022.
2 Treveil, M. et al. (5 others), "Introducing MLOps", O'Reilly Media, 2020. https://doi.org/10.48550/arXiv.1911.06270   DOI
3 Xu, J. et al. (5 others), "Federated Learning for Healthcare Informatics", 1-19, 2021. https://doi.org/10.1007/s41666-020-00082-4   DOI
4 Yang, T. et al. (7 others), "Applied federated learning: Improving google keyboard query suggestions", arXiv preprint arXiv:1812.02903, 2018. https://arxiv.org/pdf/1812.02903.pdf
5 Sculley, D. et al. (10 others), "Hidden technical debt in machine learning systems", Advances in neural information processing systems 28, 2015. https://doi.org/10.48550/arXiv.2103.10510   DOI
6 Seong-yoon, Byen, "Arrangement of MLOps in the Warring States period", MLOps KR, Announcement of the 1st online event, 2021. https://speakerdeck.com/mlopskr/mlops-cuncu-jeongug-sidae-jeongri-byeonseongyun
7 Patrick Debois, "Devops cafe Episode 12", Devopsday, 2009. http://devopscafe.org/show/2010/9/15/episode-12.html
8 https://aws.amazon.com/ko/
9 Yang, J. et al. (5 others), "Prototyping federated learning on edge computing systems", Frontiers Comput. Sci. vol. 14 no. 6: 146318, 2020. https://doi.org/10.1007/s11704-019-9237-3   DOI
10 Bonawitz, K. et al (13 others), "Towards federated learning at scale: System design", Proceedings of Machine Learning and Systems 1, 374-388. 2019. https://doi.org/10.48550/arXiv.1902.01046   DOI
11 https://kubernetes.io/ko/
12 https://argo-cd.readthedocs.io/en/stable/
13 "An Industrial Grade Federated Learning Framework. Available online", https://fate.fedai.org/ (accessed on 24 September 2021).
14 Seung-min, Lee, "Technological Trends and Industrial Implications of Federated Learning", Technology Policy Trends, 2020. https://library.etri.re.kr /service/main/index.htm?eco=open
15 Zhou, J. et al. (10 others), "A survey on federated learning and its applications for accelerating industrial internet of things", arXiv preprint arXiv:2104.10501, 2021. https://doi.org/10.48550/arXiv.2104.10501   DOI
16 Zhang, T. et al. (5 others), "Federated learning for internet of things: a federated learning framework for On-device anomaly data detection", arXiv preprint arXiv: 2106.07976, 2021. https://arxiv.org/abs/2106.07976
17 Hard, A. et al (8 others), "Federated learning for mobile keyboard prediction", arXiv preprint arXiv: 1811.03604, 2018. https://arxiv.org/pdf/1811.03604.pdf
18 Beutel, D, J. et al. (10 others), "Flower: A friendly federated learning research framework", arXiv preprint arXiv:2007.14390, 2020. https://arxiv.org/pdf/2007.14390.pdf
19 Lo, Sin K. et al. (5 others), "Architectural patterns for the design of federated learning systems", arXiv preprint arXiv:2101.02373, 2021. https://doi.org/10.48550/arXiv.2101.02373   DOI
20 Damaskinos, G. et al. (5 others), "Fleet: Online federated learning via staleness awareness and performance prediction", Proceedings of the 21st International Middleware Conference. 2020. https://doi.org/10.48550/arXiv.2006.07273   DOI
21 Kairouz, Peter, et al. (58 others), "Advances and open problems in federated learning", Foundations and Trends® in Machine Learning 14.1-2, 1-210, 2021. https://ml-ops.org/content/references.html   DOI
22 Google. "Tensorflow federated: Machine learning on decentralized data", https://www.tensorflow.org/ federated, 2021. accessed 25-Dec-21.
23 https://airflow.apache.org/
24 https://github.com/PaddlePaddle/PaddleFL
25 "Three Levels of ML Software", https://ml-ops.org/content/three-levels-of-ml-software
26 Nasron Cheong "Design a federated learning system in seven steps", https://towardsdatascience.com/design-a-federated-learning-system-in-seven-steps-d0be641949c6
27 Grafana Stack https://grafana.com/
28 https://wandb.ai/site
29 https://aws.amazon.com/ko/s3/
30 https://www.redhat.com/ko/topics/containers/whats-alinux-container
31 Zhou, Y., Yu, Y., and Ding, B., "Towards MLOps: A Case Study of ML Pipeline Platform", 2020. International Conference on Artificial Intelligence and Computer Engineering(ICAICE), 494-500, 2020. doi: 10.1109/ICAICE51518.2020.00102.   DOI
32 Qinbin, L. et al. (7 others), "A survey on federated learning systems: vision, hype and reality for data privacy and protection", IEEE Transactions on Knowledge and Data Engineering, 2021. https://doi.org/10.48550/arXiv.1907.09693   DOI
33 McMahan, B. H., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A., "Communication-efficient learning of deep networks from decentralized data", in Artificial Intelligence and Statistics. PMLR, 273-1282. https://doi.org/10.48550/arXiv.1602.05629   DOI