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Conservation of Protein Domains Over Different Proteins02:26

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  1. Home
  3. Using Class And Domain Information To Address Domain Shift In Federated Learning.
  1. Home
  3. Using Class And Domain Information To Address Domain Shift In Federated Learning.

Related Experiment Video

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Using Class and Domain Information to Address Domain Shift in Federated Learning.

Chien-Yu Chiou, Chun-Rong Huang, Lawrence L Latour

    IEEE Transactions on Neural Networks and Learning Systems
    |February 6, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Federated learning (FL) faces challenges from domain shift due to heterogeneous data. This study introduces a novel FL framework that separates and learns domain-invariant and domain-specific features, improving global model performance and robustness.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • Federated learning (FL) is challenged by heterogeneous client data distributions, leading to domain shift.
    • This domain shift causes divergent local models and degrades overall global performance in FL.
    • Existing methods struggle to effectively handle the complexities of domain variation in FL.

    Purpose of the Study:

    • To propose a novel federated learning framework addressing the domain-shift problem.
    • To decouple and collaboratively learn domain-invariant and domain-specific representations.
    • To enhance the robustness and performance of the global model against domain variations.

    Main Methods:

    • Introduced a class- and domain-aware FL framework.
  • Employed a cross-gated feature separation (CGFS) module to decouple domain and class features.
  • Utilized heterogeneous prototype contrastive learning (HPCL) for feature discriminability and a gradient-reweighted hierarchical aggregation (GHA) strategy for server aggregation.

    • Main Results:

    • The proposed framework successfully decouples and learns domain-invariant and domain-specific representations.
    • Experimental results on two FL datasets with domain shift demonstrate superior performance.
    • The method consistently outperforms existing state-of-the-art approaches in handling domain variation.

    Conclusions:

    • The proposed class- and domain-aware FL framework effectively mitigates the impact of domain shift.
    • The novel CGFS, HPCL, and GHA modules contribute to improved global model robustness and performance.
    • This approach offers a promising solution for building more resilient federated learning systems.