# HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning

arXiv: https://arxiv.org/abs/2208.07491 · HTML: https://mlsanity.com/paper/2208.07491/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2022.

## Built on by (most cited first, 6 of 6)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning](https://mlsanity.com/paper/2405.15632/) (2024)
- [Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research](https://mlsanity.com/paper/2308.06290/) (2023)
- [Interactive Hybrid Rice Breeding with Parametric Dual Projection](https://mlsanity.com/paper/2507.11848/) (2025)
- [Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach](https://mlsanity.com/paper/2502.17260/) (2025)
- [A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning](https://mlsanity.com/paper/2604.23386/) (2026)

## Abstract (arXiv)

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.