FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets
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.