A review of machine learning for big data analysis

Authors

  • Nadia Mahmood Hussien Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq
  • Samira Abdul-Kader Hussain Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq
  • Khlood Ibraheem Abbas Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq
  • Yasmin Makki Mohialden Computer Science Department, Collage of Science, Mustansiriyah University, Baghdad, Iraq

DOI:

https://doi.org/10.47667/ijpasr.v3i2.154

Keywords:

Big Data Analysis, Fedml, Machine Learning, DDB, Python

Abstract

Big data is the key to the success of many large technology companies right now. As more and more companies use it to store, analyze, and get value from their huge amounts of data, it gets harder for them to use the data they get in the best way. Most systems have come up with ways to use machine learning. In a real-time web system, data must be processed in a smart way at each node based on data that is spread out. As data privacy becomes a more important social issue, standardized learning has become a popular area of research to make it possible for different organizations to train machine learning models together while keeping privacy in mind. Researchers are becoming more interested in supporting more machine learning models that keep privacy in different ways. There is a need to build systems and infrastructure that make it easier for different standardized learning algorithms to be created. In this research, we look at and talk about the unified and distributed machine learning technology that is used to process large amounts of data. FedML is a Python program that let machine learning be used at any scale. It is a unified, distributed machine learning package.

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References

Baresi, L., Quattrocchi, G., & Rasi, N. (2021, May). Federated machine learning as a self-adaptive problem. In 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (pp. 41-47). IEEE.

Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2021). Privacy preserving distributed machine learning with federated learning. Computer Communications, 171, 112-125.

Ekmefjord, M., Ait-Mlouk, A., Alawadi, S., Åkesson, M., Stoyanova, D., Spjuth, O., ... & Hellander, A. (2021). Scalable federated machine learning with FEDn. arXiv preprint arXiv:2103.00148

He, C., Annavaram, M., & Avestimehr, S. (2020). Group knowledge transfer: Federated learning of large cnns at the edge. Advances in Neural Information Processing Systems, 33, 14068-14080.

He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., ... & Avestimehr, S. (2020). Fedml: A research library and benchmark for federated machine learning. arXiv preprint arXiv:2007.13518.

He, C., Shah, A. D., Tang, Z., Sivashunmugam, D. F. N., Bhogaraju, K., Shimpi, M., ... & Avestimehr, S. (2021). Fedcv: a federated learning framework for diverse computer vision tasks. arXiv preprint arXiv:2111.11066.

Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., ... & He, B. (2021). A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering.

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Published

2022-07-04

How to Cite

Hussien, N. M., Hussain, S. A.-K., Abbas, K. . I., & Mohialden, Y. M. (2022). A review of machine learning for big data analysis. International Journal Papier Advance and Scientific Review, 3(2), 1-4. https://doi.org/10.47667/ijpasr.v3i2.154