Privacy Leakage in Federated Learning for Smart Grid Short-Term Load Forecasting

Federated Learning (FL) for household-level Short-Term Load Forecasting (STLF) has emerged as a solution to privacy concerns in smart grids, enabling clients to collaboratively train models without sharing their consumption data with a central server. However, sharing model updates can still introduce privacy risks. A common solution to mitigate this risk is the use of differential privacy during the federation process. However, there is a lack of empirical privacy analysis of these techniques in smart grid scenarios. This paper proposes a property inference attack utilizing a single update per client to evaluate privacy risks in federated learning within smart grid contexts. We assess the privacy risk associated with the standard FedAvg algorithm and a differentially private noise-before-aggregation (NBA) FL scheme. Furthermore, we investigate the trade-off between privacy and utility in the NBA-FL scheme. Our empirical findings reveal significant information leakage with standard FedAvg scheme. An adversary with access to a single model update can identify global data properties of the FedAvg client local dataset with an Area Under the Curve (AUC) of 72%. This privacy leakage can be reduced using NBA-FL, which reduces the AUC to 60%. However, the addition of noise to the model updates results in a utility loss of up to 70% in the model’s predictive power. This significant degradation in model performance outweighs the advantages of the scheme.