  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.
