Problems, solutions and improvements on federated learning model

Research Article
Open access

Problems, solutions and improvements on federated learning model

Leqi Huang 1*
  • 1 Big Bridge Academy    
  • *corresponding author lambert_huang1123@163.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/22/20231215
ACE Vol.22
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-035-6
ISBN (Online): 978-1-83558-036-3

Abstract

The field of machine learning has been stepping forward at a significant pace since the 21century due to the continuous modifications and improvements on the major underlying algorithms, particularly the model named federated learning (FL). This paper will specifically focus on the Partially Distributed and Coordinated Model, one of the major models subject to federated learning, to provide an analysis of the model’s working algorithms, existing problems and solutions, and improvements on the original model. The identification of the merits and drawbacks of each solution will be founded on document analysis, data analysis and contrastive analysis. The research concluded that both alternative solutions and improvements to the original model can possess their unique advantage as well as newly-emerged concerns or challenges.

Keywords:

machine learning, federated learning, partially distributed and coordinated model, local epoch adjustment

Huang,L. (2023). Problems, solutions and improvements on federated learning model. Applied and Computational Engineering,22,183-186.
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References

[1]. Tian L., Talwalkar T. (2019). Federated Learning: Challenges, Methods, and Future Directions. arXiv:1908.07873v1 [cs.LG] 21 Aug 2019

[2]. Yang, C., Wang, Q., Xu, M., Chen, Z., Bian, K., Liu, Y., & Liu, X. (2020). Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data. arXiv preprint arXiv:2006.06983.

[3]. Deshpande, S. (2021, October 5). Overfitting in ML: Avoiding the pitfalls. Towards Data Science. https://towardsdatascience.com/overfitting-in-ml-avoiding-the-pitfalls-d5225b7118d

[4]. Diwangkara, S. S., & Kistijantoro, A. I. (2020). Study of data imbalance and asynchronous aggregation algorithm on federated learning system. https://ieeexplore.ieee.org/abstract/document/9264958/authors#authors

[5]. Yan Zeng, Xin Wang, Junfeng Yuan, Jilin Zhang, Jian Wan, "Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning", Wireless Communications and Mobile Computing, vol. 2022, Article ID 6887040, 15 pages, 2022. https://doi.org/10.1155/2022/6887040

[6]. Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks (Version 5) [Preprint]. arXiv. https://arxiv.org/abs/1812.06127

Cite this article

Huang,L. (2023). Problems, solutions and improvements on federated learning model. Applied and Computational Engineering,22,183-186.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.22
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Tian L., Talwalkar T. (2019). Federated Learning: Challenges, Methods, and Future Directions. arXiv:1908.07873v1 [cs.LG] 21 Aug 2019

[2]. Yang, C., Wang, Q., Xu, M., Chen, Z., Bian, K., Liu, Y., & Liu, X. (2020). Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data. arXiv preprint arXiv:2006.06983.

[3]. Deshpande, S. (2021, October 5). Overfitting in ML: Avoiding the pitfalls. Towards Data Science. https://towardsdatascience.com/overfitting-in-ml-avoiding-the-pitfalls-d5225b7118d

[4]. Diwangkara, S. S., & Kistijantoro, A. I. (2020). Study of data imbalance and asynchronous aggregation algorithm on federated learning system. https://ieeexplore.ieee.org/abstract/document/9264958/authors#authors

[5]. Yan Zeng, Xin Wang, Junfeng Yuan, Jilin Zhang, Jian Wan, "Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning", Wireless Communications and Mobile Computing, vol. 2022, Article ID 6887040, 15 pages, 2022. https://doi.org/10.1155/2022/6887040

[6]. Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks (Version 5) [Preprint]. arXiv. https://arxiv.org/abs/1812.06127

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