On the Fairness of Swarm Learning in Skin Lesion Classification
This addresses fairness issues in healthcare AI for skin lesion diagnosis, but it is incremental as it empirically evaluates an existing method without introducing new techniques.
The study examined whether Swarm Learning (SL) improves fairness in skin lesion classification, finding that SL does not worsen fairness compared to centralized training and enhances both performance and fairness over single-node training, though biases persist and implementation is more complex.
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.