iron-deficiency

This paper presents an embedding-based federated learning pipeline for predicting iron deficiency from routine blood count data, deployed across two clinical sites with non-IID distributions. It demonstrates that personalized aggregation (FedMAP) outperforms standard FedAvg and local-only training, achieving higher ROC-AUC at both sites.