2024/04/29 by Ahmed El‐Hussein, Gamze Gürsoy, Elhussein, Ahmed +1 · 1 citation
Computer Science · Neuroscience · #Advanced Graph Neural Networks #Brain Tumor Detection and Classification #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2404.18773
openalex publication_date 2024/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Federated Learning is increasingly used in domains such as healthcare to facilitate collaborative model training without data-sharing. However, datasets located in different sites are often non-identically distributed, leading to degradation of model performance in FL. Most existing methods for assessing these distribution shifts are limited by being dataset or task-specific. Moreover, these metrics can only be calculated by exchanging data, a practice restricted in many FL scenarios. To address these challenges, we propose a novel metric for assessing dataset similarity. Our metric exhibits several desirable properties for FL: it is dataset-agnostic, is calculated in a privacy-preserving manner, and is computationally efficient, requiring no model training. In this paper, we first establish a theoretical connection between our metric and training dynamics in FL. Next, we extensively evaluate our metric on a range of datasets including synthetic, benchmark, and medical imaging datasets. We demonstrate that our metric shows a robust and interpretable relationship with model performance and can be calculated in privacy-preserving manner. As the first federated dataset similarity metric, we believe this metric can better facilitate successful collaborations between sites.