Quantitative mass spectrometry has transformed proteomics by allowing the simultaneous quantification of thousands of proteins. To boost statistical power, it is necessary to increase sample sizes by combining patient-derived data from various institutions. However, this practice raises significant privacy concerns. We created a DIA-LFQ dataset containing 118 samples generated from Escherichia coli MG1655 (DSM 18039) cultures and distributed them to five independent proteomics centers. This distributed data were used as a proof of concept to introduce FedProt - the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data.