  Quantitative mass spectrometry has revolutionized proteomics by enabling
simultaneous quantification of thousands of proteins. Pooling patient-derived
data from multiple institutions enhances statistical power but raises
significant privacy concerns. Here we introduce FedProt, the first
privacy-preserving tool for collaborative differential protein abundance
analysis of distributed data, which utilizes federated learning and additive
secret sharing. In the absence of a multicenter patient-derived dataset for
evaluation, we created two, one at five centers from LFQ E.coli experiments and
one at three centers from TMT human serum. Evaluations using these datasets
confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled
data, with completely negligible absolute differences no greater than $\text{$4
\times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate
meta-analysis methods diverged from the centralized analysis results by up to
25-27. FedProt is available as a web tool with detailed documentation as a
FeatureCloud App.
