VFedA: A Vertical Federated Learning Framework with Relational Alignment for Cross-Regional False Data Injection...

Large-scale smart grids have introduced significant challenges for false data injection attack (FDIA) detection due to the distributed nature of power system operation and the limited observability of individual regions. Existing horizontal federated learning approaches enable collaborative model training without sharing raw data; however, they rely on local information and often fail to capture the global operational characteristics needed to detect sophisticated attacks. To address this limitation, the present paper proposes VFedA, a federated vertical relational alignment framework for FDIA detection via cross-regional feature fusion. Specifically, each regional client employs a self-supervised spatiotemporal contrastive learning encoder to extract local representations from measurements. The extracted representations are uploaded to a control center and concatenated to construct global feature representations for attack detection. In order to mitigate inconsistencies across heterogeneous regional feature spaces, a relational alignment mechanism is introduced to enforce structural consistency by aligning similarity matrices derived from local representations. The proposed approach enables effective cross-regional information fusion while preserving data privacy. Extensive experiments are conducted on IEEE 30-bus and IEEE 118-bus benchmark systems. The results demonstrate that VFedA consistently outperforms conventional horizontal federated learning methods and achieves performance comparable to centralized detection models.