ZTFed-MAS2S：一种用于风电数据填补的可验证隐私与信任感知聚合零信任联邦学习框架

Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust (ZT) mechanisms, where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a ZT federated learning framework that integrates a multihead attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with noninteractive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.