Federated　Learning　(FL)　enables　multiple　clients　to　collaborate　in　training　neural　network　models　while　retaining　their　private　data　locally.　Despite　its　advantages,　FL　is　vulnerable　to　backdoor　attacks　due　to　its　distributed　nature.　Attackers　introduce　triggers　into　the　global　model,　causing　it　to　make　specified　predictions　on　inputs　containing　these　triggers.　Existing　detection　or　clustering　defense　methods　based　on　distance　and　similarity　have　significant　limitations.　Methods　based　on　clipping　and　adding　noise　can　only　slightly　mitigate　the　impact　of　backdoor　attacks.　To　achieve　a　better　defense,　we　introduce　FedCL,　a　backdoor　defense　framework　that　accurately　detects　backdoor　models　by　assessing　the　uncertainty　of　model　predictions.　Additionally,　it　employs　dynamic　clipping　to　limit　model　updates＇　impact,　successfully　mitigating　backdoor　attacks　without　compromising　the　global　model＇s　accuracy.　The　experiments　indicate　that　FedCL　moderately　improves　by　0.01%↑　∼　85.78%↑　than　the　state-of-the-art　(SOTA)　defense　methods,　especially　in　the　CIFAR-10　task　trained　with　more　complex　networks.　©　2024　IEEE.