The　machine　learning　paradigms　driven　by　the　sixth-generation　network　(6G)　facilitate　an　ultra-fast　and　low-latency　communication　environment.　However,　specific　research　and　practical　applications　have　revealed　that　there　are　still　various　issues　regarding　their　applicability.　A　system　named　Incentivizing　Secure　Federated　Learning　Systems　(ISFL-Sys)　is　proposed,　consisting　of　a　blockchain　module　and　a　federated　learning　module.　A　data-security-oriented　trustworthy　federated　learning　mechanism　called　Efficient　Trustworthy　Federated　Learning　(ETFL)　is　introduced　in　the　system.　Utilizing　a　directed　acyclic　graph　as　the　ledger　for　edge　nodes,　an　incentive　mechanism　has　been　devised　through　the　use　of　smart　contracts　to　encourage　the　involvement　of　edge　nodes　in　federated　learning.　Experimental　simulations　have　demonstrated　the　efficient　security　of　the　proposed　federated　learning　mechanism.　Furthermore,　compared　to　benchmark　algorithms,　the　mechanism　showcases　improved　convergence　and　accuracy.　©　2023　by　the　authors.