Abstract
Count data, such as gene expression and microbiome composition, play a significant role in various diseases, including cancer, obesity, inflammatory bowel disease, and mental health disorders. For instance, understanding the differences in microbial abundance between patients is essential for uncovering the microbiome's impact on these conditions. Differential abundance analysis (DAA) can detect significant changes between groups of patients. However, since individuals have unique microbial fingerprints that could potentially be identifiable, microbiome data must be treated as sensitive patient data, which poses problems for collaborative studies in the medical field. In this work, we introduce gLinDA, a global differential abundance analysis tool that employs a privacy-preserving swarm learning approach for the analysis of distributed datasets. gLinDA maintains predictive performance while safeguarding patient sensitive data.