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 mechanisms where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a zero-trust federated learning framework that integrates a multi-head attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with non-interactive 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.

翻译：风电数据常因传感器故障和边缘站点传输不稳定而存在缺失值。虽然联邦学习支持在不共享原始数据的情况下进行隐私保护的协作，但其在参数交换过程中仍易受异常更新和隐私泄露的影响。这些挑战在开放的工业环境中被进一步放大，因此需要采用零信任机制，即不默认信任任何参与者。为应对这些挑战，本文提出了ZTFed-MAS2S，一种集成了基于多头注意力的序列到序列填补模型的零信任联邦学习框架。ZTFed将可验证差分隐私与非交互式零知识证明以及机密性与完整性验证机制相结合，以确保可验证的隐私保护与安全的模型参数传输。该框架采用动态信任感知聚合机制，其中信任通过相似性图进行传播以增强鲁棒性，并通过基于稀疏化和量化的压缩来降低通信开销。MAS2S模型能够捕捉风电数据中的长期依赖关系以实现精确填补。在真实风电场数据集上进行的大量实验验证了ZTFed-MAS2S在联邦学习性能和缺失数据填补方面的优越性，证明了其作为能源领域实际应用中安全高效解决方案的有效性。