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Comparative Study of Federated Learning Algorithms Based on SPADE Agents

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Intelligent Data Engineering and Automated Learning – IDEAL 2024 (IDEAL 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15347))

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Abstract

Federated Learning (FL) is a prominent method in machine learning, that ensures privacy by enabling distributed devices to collaboratively learn a shared model without exchanging local data. This paper provides a comparative analysis of various FL algorithms implemented on the Smart Python Agent Development Environment (SPADE) framework. We focus on evaluating the performance, scalability, and resilience of these algorithms across different network setups and data distribution scenarios. Our results highlight the differential impacts of decentralized versus centralized approaches, particularly under non-IID data conditions, common in real-world applications. By leveraging SPADE agents and consensus algorithms, this study not only tests algorithmic efficiency and system robustness but also explores advanced strategies like asynchronous updates and coalition-based learning, which show promise in enhancing model accuracy and reducing communication overhead.

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Notes

  1. 1.

    https://xmpp.org/.

References

  1. Carrascosa, C., Enguix, F., Rebollo, M., Rincon, J.: Consensus-based learning for mas: definition, implementation and integration in ives. IJIMAI 8(3), 21–32 (2023)

    Article  Google Scholar 

  2. Carrascosa, C., Pico, A., Matagne, M.M., Rebollo, M., Rincon, J.: Asynchronous consensus for multi-agent systems and its application to federated learning. Eng. Appl. Artif. Intell. 135, 108840 (2024)

    Article  Google Scholar 

  3. Carrascosa, C., Rincón, J., Rebollo, M.: Co-learning: consensus-based learning for multi-agent systems. In: Dignum, F., Mathieu, P., Corchado, J.M., De La Prieta, F. (eds.) PAAMS 2022, pp. 63–75. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-18192-4_6

    Chapter  Google Scholar 

  4. Enguix, F.: Gtg-col: a new decentralized federated learning based on consensus for dynamic networks. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds.) PAAMS 2023, pp. 284–295. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-37616-0_24

    Chapter  Google Scholar 

  5. Enguix, F., Carrascosa, C., Rincon, J.: Exploring federated learning tendencies using a semantic keyword clustering approach. Information 15(7) (2024). https://doi.org/10.3390/info15070379. https://www.mdpi.com/2078-2489/15/7/379

  6. Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends® Machi. Learn. 14(1–2), 1–210 (2021)

    Google Scholar 

  7. Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. CoRR arxiv:1610.05492 (2016)

  8. McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)

    Google Scholar 

  9. Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007). https://doi.org/10.1109/JPROC.2006.887293

    Article  Google Scholar 

  10. Palanca, J., Terrasa, A., Julian, V., Carrascosa, C.: Spade 3: supporting the new generation of multi-agent systems. IEEE Access 8, 182537–182549 (2020). https://doi.org/10.1109/ACCESS.2020.3027357

    Article  Google Scholar 

  11. Rebollo, M., Rincon, J.A., Hernández, L., Enguix, F., Carrascosa, C.: Extending the framework for developing intelligent virtual environments (five) with artifacts for modeling internet of things devices and a new decentralized federated learning based on consensus for dynamic networks. Sensors 24(4), 1342 (2024)

    Article  Google Scholar 

  12. Rincon, J., Julian, V., Carrascosa, C.: Flamas: federated learning based on a spade mas. Appl. Sci. 12(7), 3701 (2022)

    Article  Google Scholar 

  13. Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. arXiv preprint arXiv:2002.06440 (2020)

  14. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)

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Author information

Authors and Affiliations

  1. Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022, Valencia, Spain

    Francisco Enguix, Saúl Cerdá Peris & C. Carrascosa

  2. Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, Ctra. Orón, 28, 09200, Miranda de Ebro, Burgos, Spain

    J. A. Rincon

Authors
  1. Francisco Enguix
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  2. Saúl Cerdá Peris
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  3. J. A. Rincon
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  4. C. Carrascosa
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Corresponding author

Correspondence to Francisco Enguix .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. The University of Manchester, Manchester, UK

    Hujun Yin

  4. Universitat Politècnica de València, Valencia, Valencia, Spain

    Juan M. Alberola

  5. University of Evora, Evora, Portugal

    Vitor Beires Nogueira

  6. Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. University of Huelva, Huelva, Spain

    Antonio Tallón-Ballesteros

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Enguix, F., Peris, S.C., Rincon, J.A., Carrascosa, C. (2025). Comparative Study of Federated Learning Algorithms Based on SPADE Agents. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_33

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  • DOI: https://doi.org/10.1007/978-3-031-77738-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77737-0

  • Online ISBN: 978-3-031-77738-7

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