On the Fairness of Swarm Learning in Skin Lesion Classification

TL;DR

This study evaluates the fairness of Swarm Learning in skin lesion classification, showing it improves fairness over single training but still has biases, with a more complex implementation than alternatives.

Contribution

The paper provides an empirical comparison of fairness in Swarm Learning versus centralized and single training in healthcare applications.

Findings

Swarm Learning does not worsen fairness compared to centralized training.

SL improves both performance and fairness over single training.

Biases still exist in SL models, and implementation complexity is higher.

Abstract

in healthcare. However, the existing AI model may be biased in its decision marking. The bias induced by data itself, such as collecting data in subgroups only, can be mitigated by including more diversified data. Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources, also known as nodes. In this work, we target on examining the fairness issue in Swarm Learning (SL), a recent edge-computing based decentralized machine learning approach, which is designed for heterogeneous illnesses detection in precision medicine. SL has achieved high performance in clinical applications, but no attempt has been made to evaluate if SL can improve fairness. To address the problem, we present an empirical study by comparing the fairness among single (node) training, SL, centralized training. Specifically, we evaluate on large public available skin lesion dataset, which contains samples from various subgroups. The experiments demonstrate that SL does not exacerbate the fairness problem compared to centralized training and improves both performance and fairness compared to single training. However, there still exists biases in SL model and the implementation of SL is more complex than the alternative two strategies.