On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks

TL;DR

This paper investigates how data heterogeneity affects federated learning in healthcare, providing a formal taxonomy, evaluating algorithms, and benchmarking their performance on medical datasets to guide algorithm selection.

Contribution

It offers a comprehensive formalization of heterogeneity in federated learning, benchmarks seven algorithms on medical data, and provides guidelines for healthcare applications.

Findings

Heterogeneity significantly impacts FL performance in healthcare.

Benchmark results identify the most robust algorithms for medical data.

Guidelines for selecting FL algorithms based on data heterogeneity.

Abstract

Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. However, the inherent challenge lies in the high heterogeneity of medical data, necessitating sophisticated techniques for assessment and compensation. This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the intricacies of medical data. In particular, we address the evaluation and comparison of the most popular FL algorithms with respect to their ability to cope with quantity-based, feature and label distribution-based heterogeneity. The goal is to provide a quantitative evaluation of the impact of data heterogeneity in FL systems for healthcare networks as well as a guideline on FL algorithm selection. Our research extends beyond existing studies by benchmarking seven of the most common FL algorithms against the unique challenges posed by medical data use cases. The paper targets the prediction of the risk of stroke recurrence through a set of tabular clinical reports collected by different federated hospital silos: data heterogeneity frequently encountered in this scenario and its impact on FL performance are discussed.