FENDA-FL: Personalized Federated Learning on Heterogeneous Clinical Datasets
CoRR(2023)
摘要
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, we expand the FLamby
benchmark (du Terrail et al., 2022a) to include evaluation of personalized FL
methods and demonstrate substantive performance improvements over the original
results. Next, we advocate for a comprehensive checkpointing and evaluation
framework for FL to reflect practical settings and provide multiple comparison
baselines. Finally, we study an important ablation of PerFCL (Zhang et al.,
2022). This ablation is a natural extension of FENDA (Kim et al., 2016) to the
FL setting. Experiments conducted on the FLamby benchmarks 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,
including PerFCL.
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关键词
personalized federated learning,heterogeneous clinical
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