A Unified Federated Learning Framework for Power Data Terminals Under Privacy and Resource Constraints

Power data terminals deployed in smart-grid environments generate massive amounts of operational data, yet the sensitive nature of these data and the existence of cross-region silos make centralized model training difficult in practice. Federated learning offers a natural alternative by enabling collaborative model optimization without transferring raw data, but its direct use in power terminal scenarios is still limited by four coupled challenges: update leakage, malicious or abnormal client behavior, constrained terminal-side resources, and severe Non-IID data heterogeneity. To address these issues, we develop SFL-PDT, a hierarchical federated learning framework tailored to power data terminals. The proposed method is built on a server–edge–terminal architecture. Within this architecture, edge nodes aggregate terminal updates from relatively homogeneous regional groups and perform local robustness screening, while the central server aggregates edge-level updates across heterogeneous regions and coordinates the privacy budget schedule for protected updates. It combines adaptive privacy-aware update perturbation, robust suppression of suspicious regional updates, terminal-oriented update compression, and similarity-guided aggregation for heterogeneous data distributions. Experiments on two representative power-system tasks, load forecasting and fault diagnosis, demonstrate that SFL-PDT achieves a superior overall balance among privacy protection, robustness, efficiency, and predictive performance. Compared with the evaluated baselines, the proposed method more effectively reduces reconstruction-related leakage under different privacy budgets, lowers leakage similarity under gradient inversion attacks, and maintains robust performance when malicious clients participate. It also converges faster and more stably under heterogeneous data partitions. In addition, SFL-PDT achieves the best overall predictive results, reaching an MAE of 0.021 for load forecasting and an accuracy of 88.2% for fault diagnosis, while reducing average terminal-side local training time from 4.3 s to 2.9 s and per-round upload volume from 4.2 MB to 1.5 MB relative to FedAvg. These results suggest that SFL-PDT is a practical solution for secure, efficient, and heterogeneity-aware collaborative learning in power data terminal environments.