Privacy-preserving Federated Learning for Residential Short Term Load Forecasting

TL;DR

This paper explores how federated learning combined with privacy-preserving techniques like differential privacy and secure aggregation can enable accurate residential load forecasting while maintaining data privacy.

Contribution

It demonstrates the effectiveness of combining federated learning with privacy techniques for residential load forecasting and discusses associated challenges.

Findings

Federated learning maintains high forecasting accuracy.

Privacy-preserving techniques protect load data and models.

Combination enables secure, accurate load forecasting.

Abstract

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.