Federated Learning-Enabled Building Stock Modeling for Privacy-Preserving Embodied Carbon Benchmarking in Residential Construction

Benchmarking embodied carbon in residential building stock accurately would involve a high volume of data sharing and would pose serious privacy and competitive issues among building construction stakeholders. This study introduces a new federated learning-based building stock modeling system (FedCarbon) that can allow embodied carbon to be evaluated collaboratively without data aggregation at a central place. The architecture proposed enables construction firms, cities, and providers of construction materials to collectively train predictive models at the same time as data sovereignty is achieved via a hierarchical federated aggregation mechanism with attention-based client weighting. A differentiated privacy scheme that is adaptively calibrated on noise guarantees the privacy of individual projects and allows for statistically significant benchmarking based on heterogeneous building portfolios. The framework also includes a gradient compression scheme based on momentum, which incurs an 82.6% reduction in communication overhead over traditional federated averaging-based methods and still maintains model convergence. The effectiveness of the approach is demonstrated with the help of comprehensive validation with the UCI Energy Efficiency Dataset, which includes 768 residential building configurations, and the Embodied Carbon in European Buildings Database, which includes 2340 residential units in 12 European jurisdictions. It has been experimentally shown that FedCarbon has a 94.2% prediction accuracy (R2) on embodied carbon intensity, with a mean absolute error of 21.4 kgCO2e/m2, and that (ε, δ) differential privacy can be guaranteed with ε = 1.0 and −δ = 10−5. This structure opens up building stock knowledge and hastens industry-wide implementation of low-carbon building strategies.