跨数据集迁移超参优化，提升非独立同分布下的联邦癌症影像分类性能
Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images
- 用贝叶斯优化在中心化数据上找最优超参，再迁移至联邦学习场景
- 跨数据集平均学习率并取众数批量大小，实现良好分类准确率
- 适合关注隐私保护下医疗图像联邦学习的开发者和研究人员
癌症组织病理学深度学习训练面临临床隐私约束。联邦学习（FL）通过保持数据本地化缓解此问题，但其性能依赖于非独立同分布（non-IID）客户端数据下的超参数选择。本文探究在一种癌症影像数据集上优化的超参数是否能在非IID联邦场景中泛化。研究聚焦卵巢癌和结直肠癌的二分类任务。通过集中式贝叶斯超参数优化，并将特定数据集的最优配置迁移至非IID FL设置。主要贡献是提出一种简单跨数据集聚合启发法：对学习率进行平均，对批量大小和优化器取模态值。该组合配置实现了具有竞争力的分类性能。
原文摘要 · Abstract (English)
Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by keeping data local; however, its performance depends on hyperparameter choices under non-independent and identically distributed (non-IID) client datasets. This paper examined whether hyperparameters optimized on one cancer imaging dataset generalized across non-IID federated scenarios. We considered binary histopathology tasks for ovarian and colorectal cancers. We perform centralized Bayesian hyperparameter optimization and transfer dataset-specific optima to the non-IID FL setup. The main contribution of this study is the introduction of a simple cross-dataset aggregation heuristic by combining configurations by averaging the learning rates and considering the modal optimizers and batch sizes. This combined configuration achieves a competitive classification performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。