Network　traffic　classification　is　the　foundation　for　many　network　security　and　network　management　applications.　Recently,　to　preserve　the　privacy　of　the　data　which　are　generated　in　the　mobile　ends,　federated　learning　(FL)-based　classification　methods　are　being　proposed.　Unfortunately,　the　performance　of　FL-based　methods　can　seriously　degrade　when　the　client　data　have　skewness.　This　is　particularly　true　for　mobile　network　traffic　classification　where　the　environments　in　the　mobile　ends　are　highly　heterogeneous.　In　this　article,　we　first　conduct　a　measurement　study　on　traffic　classification　accuracy　through　FL　using　real-world　network　traffic　trace　and　we　observe　serious　accuracy　degradation　due　to　heterogeneous　environments.　We　propose　a　novel　federated　analytics　(FA)　approach,　FEAT,　to　improve　the　accuracy.　Note　that　FL　emphasizes　on　model　training,　yet　our　FA　performs　local　analytic　tasks　that　can　estimate　traffic　data　skewness　and　select　appropriate　clients　for　FL　model　training.　Our　analytics　tasks　are　performed　locally　and　in　a　federated　manner;　thus,　we　preserve　privacy　as　well.　Our　approach　has　strong　theoretical　properties　where　we　exploit　Hoeffding　inequality　to　infer　traffic　data　skewness　and　we　leverage　the　Thompson　Sampling　for　client　selection.　We　evaluate　our　approach　through　extensive　experiments　using　real-world　traffic　data　sets　QUIC　and　ISCX.　The　extensive　experiments　demonstrate　that　FEAT　can　improve　traffic　classification　accuracy　in　heterogeneous　environments.