  Horizontal federated learning (HFL) enables distributed clients to train a
shared model and keep their data privacy. In training high-quality HFL models,
the data heterogeneity among clients is one of the major concerns. However, due
to the security issue and the complexity of deep learning models, it is
challenging to investigate data heterogeneity across different clients. To
address this issue, based on a requirement analysis we developed a visual
analytics tool, HetVis, for participating clients to explore data
heterogeneity. We identify data heterogeneity through comparing prediction
behaviors of the global federated model and the stand-alone model trained with
local data. Then, a context-aware clustering of the inconsistent records is
done, to provide a summary of data heterogeneity. Combining with the proposed
comparison techniques, we develop a novel set of visualizations to identify
heterogeneity issues in HFL. We designed three case studies to introduce how
HetVis can assist client analysts in understanding different types of
heterogeneity issues. Expert reviews and a comparative study demonstrate the
effectiveness of HetVis.
