A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets

TL;DR

This paper advances personalized federated learning for clinical datasets by expanding benchmarks, proposing a comprehensive evaluation framework, and introducing a robust, extended method that outperforms existing techniques on real-world medical data.

Contribution

It expands the FLamby benchmark for personalized FL, introduces a practical evaluation framework, and extends FENDA with an ablation that improves robustness on heterogeneous clinical data.

Findings

Proposed approach outperforms existing FL methods on benchmark datasets.

Expanded FLamby benchmark enables comprehensive evaluation of personalized FL.

Open-source library facilitates reproducible FL experimentation.

Abstract

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 a comprehensive 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. To this end, an open-source library aimed at making FL experimentation simpler and more reproducible is released. Finally, we propose an important ablation of PerFCL (Zhang et al., 2022). This ablation results in a natural extension of FENDA (Kim et al., 2016) to the FL setting. Experiments conducted on the FLamby benchmark and GEMINI datasets (Verma et al., 2017) show that the proposed approach is robust to heterogeneous clinical data and often outperforms existing global and personalized FL techniques, including PerFCL.