Federated Multi‐Source Data Fusion for Semi‐Supervised Fault Detection in District Heating Substations

ABSTRACT Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low‐frequency data, privacy concerns, and battery‐constrained gateways. We propose a novel hybrid semi‐supervised federated domain adaptation architecture for fault detection in DH. We use a one‐class variational autoencoder (VAE) to leverage heterogeneous sensor streams from 434 distributed substations. First, we perform cross‐network unsupervised pre‐training on multi‐sourced data from two independent real‐world DH networks, fusing their return temperature dynamics into a robust shared manifold. Second, we leverage maintenance metadata to selectively allow verified‐normal clients for per‐round fine‐tuning of the model. Third, we drastically reduce uplink costs by compressing each client's weight delta using 10% top‐ k sparsification and demonstrate that our pipeline enables robust few‐shot finetuning with 20% of the normal operational data while retaining high detection performance. By strategically training, our method achieves F 1 and G‐mean scores of up to 97% and an AUC ≥ 99% on real‐world DH data. To our knowledge, this is the first work to study cross‐domain data fusion in the DH field for fault detection, aiming to enhance and enable effective, scalable, and energy‐efficient monitoring of substations.