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Distributed Active Client Selection With Noisy Clients Using Model Association Scores

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15118))

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Abstract

Active client selection (ACS) strategically identifies clients for model updates during each training round of federated learning. In scenarios with limited communication resources, ACS emerges as a superior alternative to random client selection, significantly improving the convergence rate. However, existing ACS methods struggle with clients providing noisy updates, e.g. those from noisy labels. To address this challenge, we present a new ACS algorithm for scenarios with unknown noisy clients. Our algorithm constructs a client sampling distribution based on the global association among model updates, which quantifies the ability of a client’s model update to align with those from other clients. By leveraging these associations, we efficiently identify and mitigate the impact of clients with substantial noise that could disrupt training. This approach is simple, computationally efficient, and requires no hyperparameter tuning. Experiments on six benchmark datasets demonstrate that conventional ACS methods fail to outperform random selection. In contrast, our approach significantly enhances convergence speed while using the same communication resources.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant (No. RS–2024–00337559, Enhancing Multi-Modal Large Language Model Adaptation through Robust and Efficient Distributed Collaborative Learning) and the Institute of Information & communications Technology Planning & Evaluation (IITP) grants (No. RS–2019–II191906, Artificial Intelligence Graduate School Program (POSTECH) and No. RS-2022-II220290, Visual Intelligence for Space-Time Understanding and Generation based on Multi-layered Visual Common Sense), all funded by the Korea government (MSIT).

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Authors and Affiliations

  1. POSTECH, Pohang, Korea

    Kwang In Kim

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  1. Kwang In Kim
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Correspondence to Kwang In Kim .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Kim, K.I. (2025). Distributed Active Client Selection With Noisy Clients Using Model Association Scores. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15118. Springer, Cham. https://doi.org/10.1007/978-3-031-73027-6_5

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  • DOI: https://doi.org/10.1007/978-3-031-73027-6_5

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