Federated　learning　is　good　for　building　better　cooperative　intelligent　transportation　system　(C-ITS).　Intellectual　property　protection　in　C-ITS　brings　many　benefits　to　all　vehicles.　Although　the　protection　of　model　intellectual　property　by　watermark　has　received　much　research　attention,　the　existing　works　only　deploy　watermark　in　centralized　models.　Due　to　the　difference　of　watermark　distribution　among　vehicles,　the　global　model　accuracy　of　watermark　in　federated　learning　is　significantly　reduced　or　the　local　watermark　is　invalid.　To　solve　these　problems,　we　propose　a　multi-party　entangled　watermark　algorithm　in　federated　learning.　Specifically,　in　the　local　training,　we　propose　a　watermark　enhancement　algorithm,　which　solves　the　problem　of　local　watermark　failure.　Then,　in　the　global　aggregation,　we　propose　an　entanglement　aggregation　algorithm,　which　solves　the　problem　of　a　great　loss　of　global　model　accuracy.　We　conduct　extensive　experiments　on　public　datasets　to　show　the　superiority　of　our　proposal.　The　results　show　that　our　scheme　can　obtain　more　than　16　%　and　31　%　advantages　in　model　accuracy　and　watermark　success　rate,　respectively,　compared　with　existing　watermark　schemes　in　federated　learning.