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EPFLU: Efficient Peer-to-Peer Federated Learning for Personalized User Models in Edge-Cloud Environments

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Abstract

As the number of IoT devices and the volume of data increase, distributed computing systems have become the primary deployment solution for large-scale Internet of Things (IoT) environments. Federated Learning (FL) is a collaborative machine learning framework that allows for model training using data from all participants while protecting their privacy. However, traditional FL suffers from low computational and communication efficiency in large-scale hierarchical cloud-edge collaborative IoT systems. Additionally, due to heterogeneity issues, not all IoT devices necessarily benefit from the global model of traditional FL, but instead require the maintenance of personalized levels in the global training process. Therefore we extend FL into a horizontal peer-to-peer (P2P) structure and introduce our P2PFL framework: Efficient Peer-to-peer Federated Learning for Users (EPFLU). EPFLU transitions the paradigms from vertical FL to horizontal P2P structure from the user perspective and incorporates personalized enhancement techniques using private information. Through horizontal consensus information aggregation and private information supplementation, EPFLU solves the weakness of traditional FL that dilutes the characteristics of individual client data and leads to model deviation. This structural transformation also significantly alleviates the original communication issues. Additionally, EPFLU has a customized simulation evaluation framework to make it more suitable for real-world large-scale IoT. Within this framework, we design extreme data distribution scenarios and conduct detailed experiments of EPFLU and selected baselines on the MNIST and CIFAR-10 datasets. The results demonstrate that the robust and adaptive EPFLU framework can consistently converge to optimal performance even under extreme data distribution scenarios. Compared with the selected vertical aggregation and horizontal transmission cumulative aggregation methods, EPFLU achieves communication improvements of 21% and 57% respectively.

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Notes

  1. 1.

    For detailed model and parameter setting, data distribution operations, and complete experimental records, please visithttps://github.com/XiangchiSong/EPFLU_P2PFL.

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Acknowledgements

This research was partly supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-2020-0-01795) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and IITP grant funded by the Korea government (MSIT) (No. RS-2024-00406245, Development of Software-Defined Infrastructure Technologies for Future Mobility).

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Authors and Affiliations

  1. School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

    Xiangchi Song, Zhaoyan Wang, KyeongDeok Baek & In-Young Ko

Authors
  1. Xiangchi Song

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  2. Zhaoyan Wang

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  3. KyeongDeok Baek

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  4. In-Young Ko

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Corresponding authors

Correspondence toXiangchi Song orIn-Young Ko.

Editor information

Editors and Affiliations

  1. University of Lugano, Lugano, Ticino, Switzerland

    Cesare Pautasso

  2. University of Orléans, Orléans, France

    Patrick Marcel

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Song, X., Wang, Z., Baek, K., Ko, IY. (2025). EPFLU: Efficient Peer-to-Peer Federated Learning for Personalized User Models in Edge-Cloud Environments. In: Pautasso, C., Marcel, P. (eds) Current Trends in Web Engineering. ICWE 2024. Communications in Computer and Information Science, vol 2188. Springer, Cham. https://doi.org/10.1007/978-3-031-75110-3_1

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