Privacy-Preserving Personnel Detection in Substations via Federated Learning with Dynamic Noise Adaptation

This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning. The framework integrates blockchain technology, the InterPlanetary File System (IPFS) for distributed storage, and a dynamic differential privacy mechanism to achieve collaborative security across the storage, service, and federated coordination layers. It accommodates both multimodal data classification and object detection tasks, enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection. This effectively mitigates the single-point failures and model leakage issues inherent in centralized architectures. A dynamically adjustable differential privacy mechanism is introduced to allocate privacy budgets according to client contribution levels and upload frequencies, achieving a personalized balance between model performance and privacy protection. Multi-dimensional experimental evaluations, including classification accuracy, F1-score, encryption latency, and aggregation latency, verify the security and efficiency of the proposed architecture. The improved CNN model achieves 72.34% accuracy and an F1-score of 0.72 in object detection and classification tasks on infrared surveillance imagery, effectively identifying typical risk events such as not wearing safety helmets and unauthorized intrusion, while maintaining an aggregation latency of only 1.58 s and a query latency of 80.79 ms. Compared with traditional static differential privacy and centralized approaches, the proposed method demonstrates significant advantages in accuracy, latency, and security, providing a new technical paradigm for efficient, secure data sharing, object detection, and privacy preservation in smart grid substations.