Client-Attentive Personalized Federated Learning for AR-Assisted Information Push in Power Emergency Maintenance

The integration of AI into power emergency maintenance faces a critical dilemma: centralized training compromises privacy, while standard Federated Learning (FL) struggles with the statistical heterogeneity (Non-IID) of industrial data. Traditional aggregation algorithms (e.g., FedAvg) treat clients solely based on sample size, failing to distinguish between critical fault data and redundant normal operational data. To address this theoretical gap, this paper proposes a Client-Attentive Personalized Federated Learning (PFAA) framework. Unlike conventional approaches, PFAA introduces a semantic-aware attention mechanism driven by “Device Health Fingerprints.” This mechanism dynamically quantifies the contribution of each client not just by data volume, but by the quality and physical relevance of their model updates relative to the global optimization objective. We implement this algorithm within a collaborative cloud-edge-end architecture to enable privacy-preserving, AR-assisted fault diagnosis. Extensive simulations demonstrate that PFAA effectively mitigates model divergence caused by data heterogeneity, achieving superior convergence speed and decision accuracy compared to rule-based and standard FL baselines.