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

• URL: http://arxiv.org/abs/2508.18318v1
• Date: Sun, 24 Aug 2025 01:50:58 GMT
• Title: ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation
• Authors: Yang Li, Hanjie Wang, Yuanzheng Li, Jiazheng Li, Zhaoyang Dong,
• Abstract summary: Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites.<n>ZTFed-MAS2S is a zero-trust federated learning framework that integrates a sequence-to-sequence imputation model.<n>Experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation.
• Score: 8.934430272818048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters [6.9736210593544]
  Energy theft poses a significant threat to the stability and efficiency of smart grids.<n>Traditional machine learning approaches for theft detection require aggregating user data.<n>We propose a privacy-preserving federated learning framework for energy theft detection.
  arXiv  Detail & Related papers  (2026-02-18T04:37:54Z)
• ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning [6.486831630436399]
  ERIS is a serverless FL framework that balances privacy and accuracy while eliminating the server bottleneck and distributing the communication load.<n>We theoretically prove that ERIS converges at the same rate as FedAvg under standard assumptions, and (ii) bounds mutual information leakage inversely with the number of aggregators.
  arXiv  Detail & Related papers  (2026-02-09T13:05:41Z)
• Robust Federated Learning for Malicious Clients using Loss Trend Deviation Detection [0.0]
  Federated Learning (FL) facilitates collaborative model training among distributed clients while ensuring that raw data remains on local devices.<n>Such clients can interfere with the training process by sending misleading updates, which can negatively affect the performance and reliability of the global model.<n>We propose the Federated Learning with Loss Trend Detection (FL-LTD), a lightweight and privacy-preserving defense framework that detects and mitigates malicious behavior by monitoring temporal loss dynamics rather than model gradients.
  arXiv  Detail & Related papers  (2026-01-28T18:09:53Z)
• DP-EMAR: A Differentially Private Framework for Autonomous Model Weight Repair in Federated IoT Systems [0.0]
  Federated Learning (FL) enables decentralized model training without sharing raw data.<n>In multi tier Federated IoT (Fed-IoT) systems, unstable connectivity and adversarial interference can silently alter transmitted parameters.<n>We propose DP-EMAR, a differentially private, error model based autonomous repair framework that detects and reconstructs transmission induced distortions.
  arXiv  Detail & Related papers  (2025-12-15T15:56:58Z)
• Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning [61.249748418757946]
  Asynchronous federated learning (FL) has recently gained attention for its enhanced efficiency and scalability.<n>We propose FedEcho, a novel framework that incorporates uncertainty-aware distillation to enhance the asynchronous FL performances.<n>We demonstrate that FedEcho consistently outperforms existing asynchronous federated learning baselines.
  arXiv  Detail & Related papers  (2025-11-25T06:25:25Z)
• GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks [56.983319121358555]
  Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors.<n>We introduce the Dual-Facet Attack (DFA), a novel threat model that concurrently undermines predictive accuracy and group fairness.<n>We propose GuardFed, a self-adaptive defense framework that maintains a fairness-aware reference model using a small amount of clean server data.
  arXiv  Detail & Related papers  (2025-11-12T13:02:45Z)
• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG)<n>FedE4RAG facilitates collaborative training of client-side RAG retrieval models.<n>We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles [6.3338980105224145]
  Existing FL frameworks struggle to balance privacy, fairness, and robustness, leading to performance disparities across demographic groups.<n>This work explores the trade-off between privacy and fairness in FL-based object detection for AVs and introduces RESFL, an integrated solution optimizing both.<n> RESFL incorporates adversarial privacy disentanglement and uncertainty-guided fairness-aware aggregation.<n>We evaluate RESFL on the FACET dataset and CARLA simulator, assessing accuracy, fairness, privacy resilience, and robustness under varying conditions.
  arXiv  Detail & Related papers  (2025-03-20T15:46:03Z)
• FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning [17.853502904387376]
  Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems.<n>We propose Federated Error Minimization (FedEM), a novel algorithm that incorporates controlled perturbations through adaptive noise injection.<n> Experimental results on benchmark datasets demonstrate that FedEM significantly reduces privacy risks and preserves model accuracy, achieving a robust balance between privacy protection and utility preservation.
  arXiv  Detail & Related papers  (2025-03-08T02:48:00Z)
• Towards Trustworthy Federated Learning [26.25193909843069]
  This paper develops a comprehensive framework to address three critical trustworthy challenges in federated learning (FL)<n>To improve the system's defense against Byzantine attacks, we develop a Two-sided Norm Based Screening mechanism.<n>We also adopt a differential privacy-based scheme to prevent raw data at local clients from being inferred by curious parties.
  arXiv  Detail & Related papers  (2025-03-05T17:25:20Z)
• ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation [91.20492150248106]
  We investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases.<n>We propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs.<n> Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• How Breakable Is Privacy: Probing and Resisting Model Inversion Attacks in Collaborative Inference [13.453033795109155]
  Collaborative inference improves computational efficiency for edge devices by transmitting intermediate features to cloud models.<n>There is no established criterion for assessing the difficulty of model inversion attacks (MIAs)<n>We propose the first theoretical criterion to assess MIA difficulty in CI, identifying mutual information, entropy, and effective information volume as key influencing factors.
  arXiv  Detail & Related papers  (2025-01-01T13:00:01Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.