Federated learning (FL) is increasingly being recognized as a key approach to
overcoming the data silos that so frequently obstruct the training and
deployment of machine-learning models in clinical settings. This work
contributes to a growing body of FL research specifically focused on clinical
applications along three important directions. First, an extension of the FENDA
method (Kim et al., 2016) to the FL setting is proposed. Experiments conducted
on the FLamby benchmarks (du Terrail et al., 2022a) and GEMINI datasets (Verma
et al., 2017) show that the approach is robust to heterogeneous clinical data
and often outperforms existing global and personalized FL techniques. Further,
the experimental results represent substantive improvements over the original
FLamby benchmarks and expand such benchmarks to include evaluation of
personalized FL methods. Finally, we advocate for a comprehensive checkpointing
and evaluation framework for FL to better reflect practical settings and
provide multiple baselines for comparison.

该研究通过扩展 FENDA 方法，提出了一种适用于临床应用的联邦学习方法，并在 FLamby 基准和 GEMINI 数据集上进行了实验证明其对异构临床数据具有鲁棒性，并通常优于现有的全局和个性化联邦学习技术。此外，实验结果对原始 FLamby 基准进行了实质性改进，并扩展了该基准以包括个性化联邦学习方法的评估。最后，该研究还提倡建立一个全面的检查点和评估框架，以更好地反映实际情况，并提供多个基准进行比较。