Federated　learning　(FL)　is　an　emerging　paradigm　for　privacy-preserving　machine　learning,　in　which　multiple　clients　collaborate　to　generate　a　global　model　through　training　individual　models　with　local　data.　However,　FL　is　vulnerable　to　model　poisoning　attacks　(MPAs)　as　malicious　clients　are　able　to　destroy　the　global　model　by　modifying　local　models.　Although　numerous　model　poisoning　defense　methods　are　extensively　studied,　they　remain　vulnerable　to　newly　proposed　optimized　MPAs　and　are　constrained　by　the　necessity　to　presume　a　certain　proportion　of　malicious　clients.　To　this　end,　in　this　paper,　we　propose　MODEL,　a　model　poisoning　defense　framework　for　FL　through　truth　discovery　(TD).　A　distinctive　aspect　of　MODEL　is　its　ability　to　effectively　prevent　both　optimized　and　byzantine　MPAs.　Furthermore,　it　requires　no　presupposed　threshold　for　different　settings　of　malicious　clients　(e.g.,　less　than　33%　or　no　more　than　50%).　Specifically,　a　TD-based　metric　and　a　clustering-based　filtering　mechanism　are　proposed　to　evaluate　local　models　and　avoid　presupposing　a　threshold.　Furthermore,　MODEL　is　effective　for　non-independent　and　identically　distributed　(non-IID)　training　data.　In　addition,　inspired　by　game　theory,　we　incorporate　a　truthful　and　fair　incentive　mechanism　in　MODEL　to　encourage　active　client　participation　while　mitigating　the　potential　desire　for　attacks　from　malicious　clients.　Extensively　comparative　experiments　demonstrate　that　MODEL　effectively　safeguards　against　optimized　MPAs　and　outperforms　the　state-of-the-art.