With　the　development　of　modern　internet　techniques,　Cross-Domain　Recommendation　(CDR)　systems　have　been　widely　exploited　for　tackling　the　data-sparsity　problem.　Meanwhile　most　current　CDR　models　assume　that　user-item　interactions　are　accessible　across　different　domains.　However,　such　knowledge　sharing　process　will　break　the　privacy　protection　policy.　In　this　paper,　we　focus　on　the　Privacy-Preserving　Multi-Domain　Recommendation　problem　(PPMDR).　The　problem　is　challenging　since　different　domains　are　sparse　and　heterogeneous　with　the　privacy　protection.　To　tackle　the　above　issues,　we　propose　Federated　Probabilistic　Preference　Distribution　Modelling　(FPPDM).　FPPDM　includes　two　main　components,　i.e.,　local　domain　modelling　component　and　global　server　aggregation　component　with　federated　learning　strategy.　The　local　domain　modelling　component　aims　to　exploit　user/item　preference　distributions　using　the　rating　information　in　the　corresponding　domain.　The　global　server　aggregation　component　is　set　to　combine　user　characteristics　across　domains.　To　better　extract　semantic　neighbors　information　among　the　users,　we　further　provide　compactness　co-clustering　strategy　in　FPPDM++　to　cluster　the　users　with　similar　characteristics.　Our　empirical　studies　on　benchmark　datasets　demonstrate　that　FPPDM/FPPDM++　significantly　outperforms　the　state-of-the-art　models.　©　2023　International　Joint　Conferences　on　Artificial　Intelligence.　All　rights　reserved.