MORPHFED: Federated Learning for Cross-institutional Blood Morphology Analysis

TL;DR

This paper presents MORPHFED, a federated learning framework for blood morphology analysis that enables collaborative, privacy-preserving model training across multiple institutions, improving generalization and robustness in diverse clinical settings.

Contribution

The paper introduces a novel federated learning approach tailored for blood cell morphology analysis, addressing privacy concerns and dataset variability across institutions.

Findings

Federated models outperform centralized models in cross-site performance.

Models learn domain-invariant features enhancing robustness.

Federated approach preserves data privacy while maintaining high accuracy.

Abstract

Automated blood morphology analysis can support hematological diagnostics in low- and middle-income countries (LMICs) but remains sensitive to dataset shifts from staining variability, imaging differences, and rare morphologies. Building centralized datasets to capture this diversity is often infeasible due to privacy regulations and data-sharing restrictions. We introduce a federated learning framework for white blood cell morphology analysis that enables collaborative training across institutions without exchanging training data. Using blood films from multiple clinical sites, our federated models learn robust, domain-invariant representations while preserving complete data privacy. Evaluations across convolutional and transformer-based architectures show that federated training achieves strong cross-site performance and improved generalization to unseen institutions compared to centralized training. These findings highlight federated learning as a practical and privacy-preserving approach for developing equitable, scalable, and generalizable medical imaging AI in resource-limited healthcare environments.