What is it about?
This work presents ZTFed-MAS2S, a new framework that combines federated learning (FL), zero-trust security principles, and advanced AI modeling to accurately fill in missing wind power data collected from multiple wind farms. In real-world wind farms, data is often missing due to sensor failures and poor communication at remote locations. The proposed solution tackles this by: 1 Using a multi-head attention-based sequence-to-sequence model (MAS2S) to accurately reconstruct missing time series data (e.g., wind power and related features). 2 Applying verifiable differential privacy with non-interactive zero-knowledge proofs to protect data privacy, even from potentially untrusted parties. 3 Introducing a dynamic trust-aware aggregation mechanism that evaluates which clients (wind farms) are providing trustworthy data updates for collaborative model training. 4 Reducing communication overhead through smart compression and secure encryption. The approach is tested on real wind datasets from the U.S. National Renewable Energy Laboratory (NREL) and shows excellent results compared to both traditional and state-of-the-art AI-based imputation methods.
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Why is it important?
This work is timely and significant for several reasons: 1 Data privacy and trust are growing concerns in the energy sector, especially with the adoption of Industrial Internet of Things (IIoT) technologies in wind farms. ZTFed-MAS2S removes the need for a central trusted authority, aligning with modern cybersecurity best practices through a zero-trust architecture. 2 Wind data often suffers from high missing rates, and traditional models cannot robustly handle such extreme scenarios. This framework accurately imputes missing values even when 90% of the data is gone, maintaining high precision. 3 It bridges cutting-edge machine learning with rigorous privacy guarantees, making it both secure and practical for real deployment in critical infrastructure. 4 The communication-efficient design ensures scalability to large, distributed systems, which is crucial for smart grids and large-scale renewable integration. 5 It’s the first known framework to integrate verifiable privacy, trust-aware aggregation, and hybrid missing pattern handling in FL for energy data, filling a clear gap in the field.
Perspectives
As a researcher, I find this paper impactful because it elegantly balances technical sophistication with real-world applicability. The combination of privacy-preserving techniques (DP-NIZK), robust aggregation (DTAA), and a powerful AI imputation model (MAS2S) makes the approach holistic and future-ready. The use of zero-knowledge proofs in federated learning is especially commendable—it ensures privacy without assuming any trust, which is critical in decentralized and adversarial environments like IIoT. Moreover, the framework’s robustness under various data missing patterns and attack scenarios makes it a strong candidate for deployment in safety-critical energy systems. Overall, this paper exemplifies how advanced AI can be responsibly and securely deployed in the renewable energy sector, which is a cornerstone of global sustainability efforts.
Professor/Clarivate Highly Cited Researcher/Associate Editor of IEEE TSG/TII/TSTE Yang Li
Northeast Electric Power University
Read the Original
This page is a summary of: ZTFed-MAS2S: A Zero-Trust Federated Learning Framework With Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation, IEEE Transactions on Industrial Informatics, January 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tii.2025.3609075.
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