Federated　learning　(FL)　enables　multiple　clients　to　collaboratively　train　deep　learning　models　while　considering　sensitive　local　datasets＇　privacy.　However,　adversaries　can　manipulate　datasets　and　upload　models　by　injecting　triggers　for　federated　backdoor　attacks　(FBA).　Existing　defense　strategies　against　FBA　consider　specific　and　limited　attacker　models,　and　a　sufficient　amount　of　noise　to　be　injected　only　mitigates　rather　than　eliminates　FBA.　To　address　these　deficiencies,　we　introduce　a　Flexible　Federated　Backdoor　Defense　Framework　(Fedward)　to　ensure　the　elimination　of　adversarial　backdoors.　We　decompose　FBA　into　various　attacks,　and　design　amplified　magnitude　sparsification　(AmGrad)　and　adaptive　OPTICS　clustering　(AutoOPTICS)　to　address　each　attack.　Meanwhile,　Fedward　uses　the　adaptive　clipping　method　by　regarding　the　number　of　samples　in　the　benign　group　as　constraints　on　the　boundary.　This　ensures　that　Fedward　can　maintain　the　performance　for　the　Non-IID　scenario.　We　conduct　experimental　evaluations　over　three　benchmark　datasets　and　thoroughly　compare　them　to　state-of-the-art　studies.　The　results　demonstrate　the　promising　defense　performance　from　Fedward,　moderately　improved　by　33%　similar　to　75%　in　clustering　defense　methods,　and　96.98%,　90.74%,　and　89.8%　for　Non-IID　to　the　utmost　extent　for　the　average　FBA　success　rate　over　MNIST,　FMNIST,　and　CIFAR10,　respectively.