By　integrating　subgraph　learning　and　federated　learning,　federated　subgraph　learning　achieves　collaborative　learning　of　subgraph　information　across　multiple　clients　while　protecting　data　privacy.　However,　due　to　different　data　collection　methods　of　clients,　graph　data　typically　exhibits　the　non-independent　and　identically　distributed(Non-IID)　characteristics.　Meanwhile,　there　are　significant　differences　in　the　structure　and　features　of　local　graph　data　across　clients.　These　factors　lead　to　difficult　convergence　and　poor　generalization　during　the　training　of　federated　subgraph　learning.　To　solve　these　problems,　a　personalized　federated　subgraph　learning　framework　with　embedding　alignment　and　parameter　activation(FSL-EAPA)　is　proposed.　First,　the　personalized　model　aggregation　is　performed　based　on　the　similarity　between　clients　to　reduce　the　interference　of　Non-IID　data　on　the　overall　model　performance.　Next,the　parameter　selective　activation　is　introduced　during　model　updates　to　handle　the　heterogeneity　of　subgraph　structural　features.　Finally,　the　updated　client　models　are　utilized　to　provide　positive　and　negative　clustering　representations　for　local　node　embeddings　to　aggregate　the　local　nodes　with　the　same　class.　Thus,　FSL-EAPA　can　fully　learn　feature　representations　of　nodes,　and　thereby　better　adapts　to　the　heterogeneous　data　distributions　across　different　clients.　Experiments　on　real-world　benchmark　graph　datasets　validate　the　effectiveness　of　FSL-EAPA.　The　results　show　that　FSL-EAPA　achieves　higher　classification　accuracy　under　various　scenarios.　©　2025　Science　Press.　All　rights　reserved.