{
  "annotation_id": "d541f345-9360-414d-b04a-50f7e7cf9bc6",
  "date_created": "2026-02-17T05:53:08.236000Z",
  "date_modified": "2026-02-17T05:53:08.236000Z",
  "file_hash": "6411f9c06dc6c2fa188133e11c2166467f32b5eeb23fc51b332321feb84597ca",
  "private": false,
  "record": {
    "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\u0027s 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.",
    "arxiv_id": "2601.06348",
    "authors": [
      "Siqi Zhu",
      "Joshua D. Kaggie"
    ],
    "categories": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Federated Learning and Class Imbalances",
    "url": "https://arxiv.org/abs/2601.06348",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "3cbab999-2088-4bf9-8cdb-3ac149d474a0",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}