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<p>Federated learning (FL) has rapidly gained traction for its capacity to facilitate collaborative model training across decentralized data sources while maintaining data privacy. Addressing the challenges stemming from intrinsic heterogeneity in local data distributions, personalization has become an essential aspect in advancing FL techniques. In this study, we introduce a new personalized federated learning (PFL) framework for recovering large-scale response-predictor association networks, named <b>s</b>parse <b>c</b>lustered <b>a</b>ssociation <b>le</b>arning (SCALE). This approach subtly incorporates sparse orthogonal factor learning and fusion penalization, enabling the joint exploration of latent relationships and structured clustering. To operationalize SCALE, we develop the PerFL-LSAN algorithm, tailored specifically for large-scale association networks recovery within federated settings. Theoretical analyses confirm the convergence guarantee of the PerFL-LSAN algorithm and the statistical consistency of the proposed estimator. We further validate the practical effectiveness of the new method through multiple simulation experiments and a real-world application on the Communities and Crime dataset.</p>

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Personalized federated learning on large-scale association networks

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Letian Li,
  • Runlin Zhou,
  • Zemin Zheng

摘要

Federated learning (FL) has rapidly gained traction for its capacity to facilitate collaborative model training across decentralized data sources while maintaining data privacy. Addressing the challenges stemming from intrinsic heterogeneity in local data distributions, personalization has become an essential aspect in advancing FL techniques. In this study, we introduce a new personalized federated learning (PFL) framework for recovering large-scale response-predictor association networks, named sparse clustered association learning (SCALE). This approach subtly incorporates sparse orthogonal factor learning and fusion penalization, enabling the joint exploration of latent relationships and structured clustering. To operationalize SCALE, we develop the PerFL-LSAN algorithm, tailored specifically for large-scale association networks recovery within federated settings. Theoretical analyses confirm the convergence guarantee of the PerFL-LSAN algorithm and the statistical consistency of the proposed estimator. We further validate the practical effectiveness of the new method through multiple simulation experiments and a real-world application on the Communities and Crime dataset.