Federated　Learning　(FL)　eliminates　data　silos　that　hinder　digital　transformation　while　training　a　shared　global　model　collaboratively.　However,　training　a　global　model　in　the　context　of　FL　has　been　highly　susceptible　to　heterogeneity　and　privacy　concerns　due　to　discrepancies　in　data　distribution,　which　may　lead　to　potential　data　leakage　from　uploading　model　updates.　Despite　intensive　research　on　above-identical　issues,　existing　approaches　fail　to　balance　robustness　and　privacy　in　FL.　Furthermore,　limiting　model　updates　or　iterative　clustering　tends　to　fall　into　local　optimum　problems　in　heterogeneous　(Non-IID)　scenarios.　In　this　work,　to　address　these　deficiencies,　we　provide　lightweight　privacy-preserving　cross-cluster　federated　learning　(PrivCrFL)　on　Non-IID　data,　to　trade　off　robustness　and　privacy　in　Non-IID　settings.　Our　PrivCrFL　exploits　secure　one-shot　hierarchical　clustering　with　cross-cluster　shifting　for　optimizing　sub-group　convergences.　Furthermore,　we　introduce　intra-cluster　learning　and　inter-cluster　learning　with　separate　aggregation　for　mutual　learning　between　each　group.　We　perform　extensive　experimental　evaluations　on　three　benchmark　datasets　and　compare　our　results　with　state-of-the-art　studies.　The　findings　indicate　that　PrivCrFL　offers　a　notable　performance　enhancement,　with　improvements　ranging　from　0.26%　up　arrow　to　1.35%　up　arrow　across　different　Non-IID　settings.　PrivCrFL　also　demonstrates　a　superior　communication　compression　ratio　in　secure　aggregation,　outperforming　current　state-of-the-art　works　by　10.59%.