Addressing　privacy　concerns　and　the　evolving　nature　of　user　preferences,　it　is　crucial　to　explore　collaborative　training　methods　for　federated　recommendation　models　that　match　the　performance　of　centralized　models　while　preserving　user　privacy.　Existing　federated　recommendation　models　primarily　rely　on　static　relational　data,　overlooking　the　temporal　patterns　that　dynamically　evolve　over　time.　In　domains　like　travel　recommendations,　factors　such　as　the　availability　of　attractions,　introduction　of　new　activities,　and　media　coverage　constantly　change,　influencing　user　preferences.　To　tackle　these　challenges,　we　propose　a　novel　approach　called　FedNTF.　It　leverages　an　LSTM　encoder　to　capture　multidimensional　temporal　interactions　within　relational　data.　By　incorporating　tensor　factorization　and　multilayer　perceptrons,　we　project　users　and　items　into　a　latent　space　with　time　encoding,　enabling　the　learning　of　nonlinear　relationships　among　diverse　latent　factors.　This　approach　not　only　addresses　the　privacy　concerns　by　preserving　the　confidentiality　of　user　data　but　also　enables　the　modeling　of　temporal　dynamics　to　enhance　the　accuracy　and　relevance　of　recommendations　over　time.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.