Abstract
Rapid technological progress now enables large-scale generation of single-cell data. Many laboratories can produce single-cell transcriptomic profiles from diverse tissues. A key step in single-cell analysis is unsupervised clustering followed by cell-type annotation, yet there is no agreement on marker genes, and annotation is typically done manually, making it irreproducible and poorly scalable. Privacy constraints in human datasets further complicate data sharing. There is a need for standardized, automated, and privacy-preserving cell-type annotation across datasets. We developed SwarmMAP, which applies Swarm Learning to train machine-learning models for cell-type classification in a decentralized setting without exchanging raw data between centers. SwarmMAP achieves F1-scores of 0.93, 0.98, and 0.88 in heart, lung, and breast datasets, respectively. Swarm Learning models reach an average performance of 0.907, comparable to models trained on centralized data (p-val = 0.937, Mann-Whitney U Test). Increasing the number of datasets improves prediction accuracy and supports classification across broader cell-type diversity. These results show that Swarm Learning provides an effective approach for automated cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP .