用协同蒸馏解决医疗联邦学习中的类别不平衡问题
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
- 客户端间互相蒸馏知识，避免中心服务器依赖
- 在极端不平衡数据下仍保持高准确率且方差最小
- 适合医疗图像等小样本类别多的联邦学习场景
联邦学习（FL）是一种分布式机器学习范式，可在保护数据隐私的前提下实现多方协作建模。然而，因各医疗机构资源分布不均导致的数据类别不平衡问题，常使模型偏向多数类。在医疗影像场景中，这种偏差尤为突出。为此，我们提出一种基于协同蒸馏的联邦学习框架，摒弃传统以服务器为中心的结构，推动客户端间直接知识共享，共同提升模型性能。实验表明，在医疗联邦环境中，该方法在处理类别不平衡方面显著优于其他基准方案。此外，随着类别不平衡程度加剧，本框架仍保持最低的标准差，展现出更强的鲁棒性。
原文摘要 · Abstract (English)
Federated Learning (FL) is a pioneering approach in distributed machine learning, enabling collaborative model training across multiple clients while retaining data privacy. However, the inherent heterogeneity due to imbalanced resource representations across multiple clients poses significant challenges, often introducing bias towards the majority class. This issue is particularly prevalent in healthcare settings, where hospitals acting as clients share medical images. To address class imbalance and reduce bias, we propose a co-distillation driven framework in a federated healthcare setting. Unlike traditional federated setups with a designated server client, our framework promotes knowledge sharing among clients to collectively improve learning outcomes. Our experiments demonstrate that in a federated healthcare setting, co-distillation outperforms other federated methods in handling class imbalance. Additionally, we demonstrate that our framework has the least standard deviation with increasing imbalance while outperforming other baselines, signifying the robustness of our framework for FL in healthcare.
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