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A Bayesian Federated Learning Framework With Online Laplace Approximation.

Liangxi Liu, Xi Jiang, Feng Zheng

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    Summary

    This study introduces a new federated learning (FL) framework using Laplace approximation to reduce errors and forgetting in heterogeneous data settings. The novel approach achieves state-of-the-art results on multiple benchmarks.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Distributed Systems

    Background:

    • Federated learning (FL) enables collaborative model training without data sharing.
    • Current FL methods often average parameters, leading to aggregation errors and local forgetting, especially with heterogeneous data.

    Purpose of the Study:

    • To propose a novel federated learning framework addressing aggregation errors and local forgetting.
    • To improve model performance in heterogeneous data environments.

    Main Methods:

    • Utilizes online Laplace approximation for posterior approximation on both client and server sides.
    • Employs a multivariate Gaussian product mechanism on the server to construct and maximize a global posterior.
    • Introduces a prior loss on the client side, guided by global posterior parameters, to mitigate local forgetting.

    Main Results:

    • Significantly reduces aggregation errors caused by discrepancies between local models.
    • Effectively mitigates local forgetting by binding learning constraints from other clients.
    • Achieves state-of-the-art performance across several benchmark datasets.

    Conclusions:

    • The proposed federated learning framework demonstrates superior performance compared to existing methods.
    • Online Laplace approximation and a novel client-side prior loss effectively address challenges in heterogeneous FL settings.