测试主流联邦学习方法在数据不平衡下的表现，验证其鲁棒性。
Federated Learning and Class Imbalances
- 统一框架复现并对比多种联邦学习算法
- 在乳腺影像数据集上验证算法性能，涵盖真实场景噪声
- 基于NVFlare实现可扩展部署，代码模块化易用
联邦学习（FL）可在保护数据隐私的前提下实现跨分布式设备的协同建模，但实际应用中常面临标签噪声与非独立同分布（non-IID）等数据不平衡问题。本文系统评估了当前先进方法RHFL+在类不平衡条件下的鲁棒性，主要贡献包括：（1）建立统一评估框架，复现并对比多种基准算法；（2）将RHFL+拓展至真实医疗影像数据集，如CBIS-DDSM、BreastMNIST和BHI，覆盖多样化临床场景；（3）基于NVIDIA的生产级框架NVFlare实现全新部署方案，支持模块化设计与可扩展架构。通过大量消融实验、不同噪声条件下的算法对比及客户端数量增长的可扩展性测试，全面验证了方法的有效性。
原文摘要 · Abstract (English)
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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