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Federated learning (FL) enhanced by local differential privacy (LDP) has gained promising privacy-preserving capabilities against privacy attacks on local contributions. In this context, noise-discounting LDP methods have been widely investigated to provide better model performance and stronger privacy guarantees. However, prior art calibrate privacy guarantees by distinct LDP definitions, resulting in non-uniform privacy-preserving capabilities. In this paper, aligned with the standard LDP definition, we proposed QP-LDP, a noise-discounting algorithm for FL, which can yield better model performance without any privacy loss. Specifically, QP-LDP precisely disturbs non-common components of quantized local contributions, which are selected by an extended multi-party private set intersection process. In particular, QP-LDP can comprehensively protect two types of local contributions, i.e., local models and …