Network　traffic　classifiers　of　mobile　devices　are　widely　learned　with　federated　learning(FL)　for　privacy　preservation.　Noisy　labels　commonly　occur　in　each　device　and　deteriorate　the　accuracy　of　the　learned　network　traffic　classifier.　Existing　noise　elimination　approaches　attempt　to　solve　this　by　detecting　and　removing　noisy　labeled　data　before　training.　However,　they　may　lead　to　poor　performance　of　the　learned　classifier,　as　the　remaining　traffic　data　in　each　device　is　few　after　noise　removal.　Motivated　by　the　observation　that　the　data　feature　of　the　noisy　labeled　traffic　data　is　clean　and　the　underlying　true　distribution　of　the　noisy　labeled　data　is　statistically　close　to　the　clean　traffic　data,　we　propose　to　utilize　the　noisy　labeled　data　by　normalizing　it　to　be　close　to　the　clean　traffic　data　distribution.　Specifically,　we　first　formulate　a　distributionally　robust　federated　network　traffic　classifier　learning　problem　(DR-NTC)　to　jointly　take　the　normalized　traffic　data　and　clean　data　into　training.　Then　we　specify　the　normalization　function　under　Wasserstein　distance　to　transform　the　noisy　labeled　traffic　data　into　a　certified　robust　region　around　the　clean　data　distribution,　and　we　reformulate　the　DR-NTC　problem　into　an　equivalent　DR-NTC-W　problem.　Finally,　we　design　a　robust　federated　network　traffic　classifier　learning　algorithm,　RFNTC,　to　solve　the　DR-NTC-W　problem.　Theoretical　analysis　shows　the　robustness　guarantee　of　RFNTC.　We　evaluate　the　algorithm　by　training　classifiers　on　a　real-world　dataset.　Our　experimental　results　show　that　RFNTC　significantly　improves　the　accuracy　of　the　learned　classifier　by　up　to　1.05　times.