2024-11-25-tavakoli24a.md

title	abstract	openreview	layout	series	publisher	issn	id	month	tex_title	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
A Comprehensive View of 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, 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.	btijACJ4QU	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	tavakoli24a	0	A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets	false	Tavakoli, Fatemeh and Emerson, D. B. and Ayromlou, Sana and Jewell, John Taylor and Krishnan, Amrit and Zhang, Yuchong and Verma, Amol and Razak, Fahad	given family Fatemeh Tavakoli given family D. B. Emerson given family Sana Ayromlou given family John Taylor Jewell given family Amrit Krishnan given family Yuchong Zhang given family Amol Verma given family Fahad Razak	2024-11-25		Proceedings of the 9th Machine Learning for Healthcare Conference	252	inproceedings	date-parts 2024 11 25	https://raw.githubusercontent.com/mlresearch/v252/main/assets/tavakoli24a/tavakoli24a.pdf