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Communication-efficient distributed EM algorithm

Author

Listed:
  • Xirui Liu

    (Beijing University of Technology)

  • Mixia Wu

    (Beijing University of Technology)

  • Liwen Xu

    (North China University of Technology)

Abstract

The Expectation Maximization (EM) algorithm is widely used in latent variable model inference. However, when data are distributed across various locations, directly applying the EM algorithm can often be impractical due to communication expenses and privacy considerations. To address these challenges, a communication-efficient distributed EM algorithm is proposed. Under mild conditions, the proposed estimator achieves the same mean squared error bound as the centralized estimator. Furthermore, the proposed method requires only one extra round of communication compared to the Average estimator. Numerical simulations and a real data example demonstrate that the proposed estimator significantly outperforms the Average estimator in terms of mean squared errors.

Suggested Citation

  • Xirui Liu & Mixia Wu & Liwen Xu, 2024. "Communication-efficient distributed EM algorithm," Statistical Papers, Springer, vol. 65(9), pages 5575-5592, December.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:9:d:10.1007_s00362-024-01594-6
    DOI: 10.1007/s00362-024-01594-6
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    References listed on IDEAS

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    3. Cheng Li & Sanvesh Srivastava & David B. Dunson, 2017. "Simple, scalable and accurate posterior interval estimation," Biometrika, Biometrika Trust, vol. 104(3), pages 665-680.
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