Federated　learning　(FL)　allows　multiple　clients　to　train　deep　learning　models　collaboratively　while　protecting　sensitive　local　datasets.　However,　FL　has　been　highly　susceptible　to　security　for　federated　backdoor　attacks　(FBA)　through　injecting　triggers　and　privacy　for　potential　data　leakage　from　uploaded　models　in　practical　application　scenarios.　FBA　defense　strategies　consider　specific　and　limited　attacker　models,　and　a　sufficient　amount　of　noise　injected　can　only　mitigate　rather　than　eliminate　the　attack.　To　address　these　deficiencies,　we　introduce　a　Robust　Federated　Backdoor　Defense　Scheme　(RFBDS)　and　Privacy　preserving　RFBDS　(PrivRFBDS)　to　ensure　the　elimination　of　adversarial　backdoors.　Our　RFBDS　to　overcome　FBA　consists　of　amplified　magnitude　sparsification,　adaptive　OPTICS　clustering,　and　adaptive　clipping.　The　experimental　evaluation　of　RFBDS　is　conducted　on　three　benchmark　datasets　and　an　extensive　comparison　is　made　with　state-of-the-art　studies.　The　results　demonstrate　the　promising　defense　performance　from　RFBDS,　moderately　improved　by　31.75%　similar　to　73.75%　in　clustering　defense　methods,　and　0.03%　similar　to　56.90%　for　Non-IID　to　the　utmost　extent　for　the　average　FBA　success　rate　over　MNIST,　FMNIST,　and　CIFAR10.　Besides,　our　privacy-preserving　shuffling　in　PrivRFBDS　maintains　is　7.83e-5　similar　to　0.42x　that　of　state-of-the-art　works.