There　is　substantial　attention　to　federated　learning　with　its　ability　to　train　a　powerful　global　model　collaboratively　while　protecting　data　privacy.　Despite　its　many　advantages,　federated　learning　is　vulnerable　to　backdoor　attacks,　where　an　adversary　injects　malicious　weights　into　the　global　model,　making　the　global　model＇s　targeted　predictions　incorrect.　Existing　defenses　based　on　identifying　and　eliminating　malicious　weights　ignore　the　similarity　variation　of　the　local　weights　during　iterations　in　the　malicious　model　detection　and　the　presence　of　benign　weights　in　the　malicious　model　during　the　malicious　local　weight　elimination,　resulting　in　a　poor　defense　and　a　degradation　of　global　model　accuracy.　In　this　paper,　we　defend　against　backdoor　attacks　from　the　perspective　of　local　models.　First,　a　malicious　model　detection　method　based　on　interpretability　techniques　is　proposed.　The　method　appends　a　sampling　check　after　clustering　to　identify　malicious　models　accurately.　We　further　design　a　malicious　local　weight　elimination　method　based　on　local　weight　contributions.　This　method　preserves　the　benign　weights　in　the　malicious　model　to　maintain　their　contributions　to　the　global　model.　Finally,　we　analyze　the　security　of　the　proposed　method　in　terms　of　model　closeness　and　then　verify　the　effectiveness　of　the　proposed　method　through　experiments.　In　comparison　with　existing　defenses,　the　results　show　that　BADFL　improves　the　global　model　accuracy　by　23.14%　while　reducing　the　attack　success　rate　to　0.04%　in　the　best　case.