Federated Learning and Class Imbalances

• URL: http://arxiv.org/abs/2601.06348v1
• Date: Fri, 09 Jan 2026 23:06:37 GMT
• Title: Federated Learning and Class Imbalances
• Authors: Siqi Zhu, Joshua D. Kaggie,
• Abstract summary: Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy.<n>Real-world FL deployments face critical challenges such as data imbalances, including label noise and non-IID distributions.<n> RHFL+, a state-of-the-art method, was proposed to address these challenges in settings with heterogeneous client models.
• Score: 2.22595051078978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, real-world FL deployments face critical challenges such as data imbalances, including label noise and non-IID distributions. RHFL+, a state-of-the-art method, was proposed to address these challenges in settings with heterogeneous client models. This work investigates the robustness of RHFL+ under class imbalances through three key contributions: (1) reproduction of RHFL+ along with all benchmark algorithms under a unified evaluation framework; (2) extension of RHFL+ to real-world medical imaging datasets, including CBIS-DDSM, BreastMNIST and BHI; (3) a novel implementation using NVFlare, NVIDIA's production-level federated learning framework, enabling a modular, scalable and deployment-ready codebase. To validate effectiveness, extensive ablation studies, algorithmic comparisons under various noise conditions and scalability experiments across increasing numbers of clients are conducted.

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