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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.

ZTFed-MAS2S: A Zero-Trust Federated Learning Framework With Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation