  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.
