Federated learning (FL) effectively addresses data silos but remains vulnerable to Byzantine attacks and privacy leakage, particularly in the presence of untrusted servers. While existing robust aggregation algorithms mitigate the impact of malicious participants, accurately identifying them without compromising privacy remains a significant challenge. To address these issues, this paper proposes PBDFL, an efficient privacy-preserving and Byzantine-robust scheme for decentralized federated learning.