A Universal Metric of Dataset Similarity for Cross-silo Federated Learning

arXiv:2404.18773v1 Announce Type: new
Abstract: 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 …

abstract arxiv collaborative cs.lg data dataset datasets distributed distribution domains federated learning healthcare however performance training type universal