Federated　learning,　a　distributed　machine　learning　paradigm,　has　gained　a　lot　of　attention　due　to　its　inherent　privacy　protection　capability　and　heterogeneous　collaboration.　However,　recent　studies　have　revealed　a　potential　privacy　risk　known　as　“gradient　leakage”,　where　the　gradients　can　be　used　to　determine　whether　a　data　record　with　a　specific　property　is　included　in　another　participant’s　batch,　thereby　exposing　the　participant’s　training　data.　Current　privacy-enhanced　federated　learning　methods　may　have　drawbacks　such　as　reduced　accuracy,　computational　overhead,　or　new　insecurity　factors.　To　address　this　issue,　a　differential　privacy-enhanced　generative　adversarial　network　model　was　proposed,　which　introduced　an　identifier　into　vanilla　GAN,　thus　enabling　the　input　data　to　be　approached　while　satisfying　differential　privacy　constraints.　Then　this　model　was　applied　to　the　federated　learning　framework,　to　improve　the　privacy　protection　capability　without　compromising　model　accuracy.　The　proposed　method　was　verified　through　simulations　under　the　client/server　(C/S)　federated　learning　architecture　and　was　found　to　balance　data　privacy　and　practicality　effectively　compared　with　the　DP-SGD　method.　Besides,　the　usability　of　the　proposed　model　was　theoretically　analyzed　under　a　peer-to-peer　(P2P)　architecture,　and　future　research　work　was　discussed.　©　2023　National　Academy　of　Pediatric　Science　and　Innovation.　All　rights　reserved.