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Federated learning is a distributed paradigm that enables collaborative training across multiple clients while preserving data privacy. However, in practice, it often encounters challenges such as heterogeneous client computing capabilities and varying communication conditions. Additionally, differences in user preferences, data acquisition environments, and devices lead to non-identical data distributions and diverse research tasks across clients. To address these heterogeneity challenges, this paper proposes a constrained Federated Learning Partial Training algorithm (cFedPT), motivated by variations in client data distributions. The algorithm dynamically adjusts the participating submodels in each training round and enables clients with heterogeneous resources to engage in federated learning. Experimental results on the ISIC2019 image dataset and the ICBHI audio dataset demonstrate that cFedPT achieves superior performance compared to baseline models.

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Constrained Adaptive Partial Training for Federated Learning on Heterogeneous Clients

  • 会议论文

  • 发表日期：

    2026年

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  • Mohan Xu,
  • Lena Wiese

摘要

Federated learning is a distributed paradigm that enables collaborative training across multiple clients while preserving data privacy. However, in practice, it often encounters challenges such as heterogeneous client computing capabilities and varying communication conditions. Additionally, differences in user preferences, data acquisition environments, and devices lead to non-identical data distributions and diverse research tasks across clients. To address these heterogeneity challenges, this paper proposes a constrained Federated Learning Partial Training algorithm (cFedPT), motivated by variations in client data distributions. The algorithm dynamically adjusts the participating submodels in each training round and enables clients with heterogeneous resources to engage in federated learning. Experimental results on the ISIC2019 image dataset and the ICBHI audio dataset demonstrate that cFedPT achieves superior performance compared to baseline models.