Federated　graph　learning　has　been　widely　used　in　distributed　graph　machine　learning　tasks.　The　data　distribution　of　existing　graph-based　federated　Spatio-temporal　prediction　methods　is　mainly　segmented　by　graph　topology.　However,　in　the　real-world　Spatio-temporal　traffic　speed　prediction　task,　a　location　will　have　data　from　different　devices　belonging　to　different　companies.　A　node　may　have　multi-party　information　in　the　real-world　distributed　traffic　speed　prediction　scenario.　The　difference　in　multi-party　information　leads　to　the　information　not　being　fully　utilized.　Moreover,　the　direct　transmission　of　node　embedding　in　the　federated　learning　process　may　also　risk　privacy　leaks.　Using　homomorphic　encryption　and　other　encryption　methods　will　bring　a　high　computational　overhead.　Therefore　we　propose　a　new　distributed　privacy-preserving　traffic　speed　prediction　algorithm,　which　uses　secure　node　attribute　aggregation　strategy(SNAAS)　to　apply　to　the　multi-party　collaborative　traffic　speed　prediction　scenario　when　the　graph　topology　structure　is　public.　At　the　same　time,　secret　sharing　technology　is　used　in　SNAAS　to　protect　the　attribute　matrix　and　reduce　the　overhead　of　secret　computing.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.