Due　to　the　robust　representational　capabilities　of　graph　data,　employing　graph　neural　networks　for　its　processing　has　demonstrated　superior　performance　over　conventional　deep　learning　algorithms.　Graph　data　encompasses　abundant　features　and　structural　information;　however,　its　large-scale　collection　is　often　challenging　in　practice.　This　difficulty　arises　because　data　predominantly　exists　in　isolated　compartments,　making　it　arduous　to　harmonize　information　across　various　organizations　or　to　enable　multiple　organizations　to　collaborate　effectively　while　safeguarding　local　data　privacy.　In　light　of　an　extreme　data　distribution　scenario,　where　each　client　possesses　distinct　nodes　with　partially　overlapping　segments　yet　divergent　data　features,　we　introduce　a　dual-cloud　server　architecture.　This　framework　encompasses　the　design　of　four　secure　subprotocols:　ReEnc　(secure　re-encryption),　SecPSI　(secure　outsourcing　of　PSI),　SecWeight　(secure　weight　calculation),　and　SecAgg　(secure　aggregation).　Together,　these　components　facilitate　a　vertical　federated　learning　framework　for　graph　convolutional　networks,　ensuring　privacy　preservation.　We　provide　a　security　proof　for　the　entire　system　and　extensive　evaluation　on　three　benchmark　datasets　(Cora,　Citeseer,　and　Pubmed)　illustrates　that　our　Vertical　Federated　Graph　Convolutional　Network　(VFGCN)　surpasses　existing　privacy-preserving　methodologies.　©　2004-2012　IEEE.