Privacy-Preserving Federated Learning for Household Characteristic Identification

This work presents a privacy-preserving training framework for household characteristic identification from electricity consumption data. The proposed framework integrates two main components: (i) a synthetic data generation pipeline capable of replicating realistic energy traces from diverse family compositions, capturing fine-grained sociodemographic attributes such as household size, employment status, age groups, and home occupancy patterns; (ii) a training strategy based on Federated Learning (FL) secured with homomorphic encryption, enabling collaborative model training while preserving data ownership. Our synthetic dataset enables the performance assessment of different training scenarios, including siloed model training by individual energy utilities and secure collaboration via FL. Experimental results show that siloed training leads to inconsistent and suboptimal performance, while privacy-preserving FL achieves accuracy comparable to conventional centralized training—an ideal yet not viable option due to data regulation constraints. Our findings highlight the effectiveness of FL as a secure solution for collaborative sociodemographic profiling in smart grids.