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BAFFLE: A Baseline of Backpropagation-Free Federated Learning

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15133))

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Abstract

Federated learning (FL) is a general principle for decentralized clients to train a server model collectively without sharing local data. FL is a promising framework with practical applications, but its standard training paradigm requires the clients to backpropagate through the model to compute gradients. Since these clients are typically edge devices and not fully trusted, executing backpropagation on them incurs computational and storage overhead as well as white-box vulnerability. In light of this, we develop backpropagation-free federated learning, dubbed BAFFLE, in which backpropagation is replaced by multiple forward processes to estimate gradients. BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server. Empirically we use BAFFLE to train deep models from scratch or to finetune pretrained models, achieving acceptable results.

H. Feng—Work done during an internship at Sea AI Lab.

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Acknowledgments

This paper is supported by the National Science Foundation of China (62132017), Zhejiang Provincial Natural Science Foundation of China (LD24F020011).

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Authors and Affiliations

  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

    Haozhe Feng & Wei Chen

  2. Sea AI Lab, Singapore, Singapore

    Tianyu Pang, Chao Du, Shuicheng Yan & Min Lin

  3. Tencent Inc., Shenzhen, China

    Haozhe Feng

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  1. Haozhe Feng
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  2. Tianyu Pang
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  4. Wei Chen
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Correspondence to Tianyu Pang or Wei Chen .

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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, Hessen, Germany

    Stefan Roth

  4. Princeton University, Palo Alto, CA, 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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Feng, H., Pang, T., Du, C., Chen, W., Yan, S., Lin, M. (2025). BAFFLE: A Baseline of Backpropagation-Free Federated Learning. 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 15133. Springer, Cham. https://doi.org/10.1007/978-3-031-73226-3_6

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