Reconstruction　attacks　against　federated　learning　(FL)　aim　to　reconstruct　users’　samples　through　users’　uploaded　gradients.　Local　differential　privacy　(LDP)　is　regarded　as　an　effective　defense　against　various　attacks,　including　sample　reconstruction　in　FL,　where　gradients　are　clipped　and　perturbed.　Existing　attacks　are　ineffective　in　FL　with　LDP　since　clipped　and　perturbed　gradients　obliterate　most　sample　information　for　reconstruction.　Besides,　existing　attacks　embed　additional　sample　information　into　gradients　to　improve　the　attack　effect　and　cause　gradient　expansion,　leading　to　a　more　severe　gradient　clipping　in　FL　with　LDP.　In　this　paper,　we　propose　a　sample　reconstruction　attack　against　LDP-based　FL　with　any　target　models　to　reconstruct　victims’　sensitive　samples　to　illustrate　that　FL　with　LDP　is　not　flawless.　Considering　gradient　expansion　in　reconstruction　attacks　and　noise　in　LDP,　the　core　of　the　proposed　attack　is　gradient　compression　and　reconstructed　sample　denoising.　For　gradient　compression,　an　inference　structure　based　on　sample　characteristics　is　presented　to　reduce　redundant　gradients　against　LDP.　For　reconstructed　sample　denoising,　we　artificially　introduce　zero　gradients　to　observe　noise　distribution　and　scale　confidence　interval　to　filter　the　noise.　Theoretical　proof　guarantees　the　effectiveness　of　the　proposed　attack.　Evaluations　show　that　the　proposed　attack　is　the　only　attack　that　reconstructs　victims’　training　samples　in　LDP-based　FL　and　has　little　impact　on　the　target　model’s　accuracy.　We　conclude　that　LDP-based　FL　needs　further　improvements　to　defend　against　sample　reconstruction　attacks　effectively.　©　2025　IEEE.　All　rights　reserved.