  Accurate load forecasting is crucial for energy management, infrastructure
planning, and demand-supply balancing. Smart meter data availability has led to
the demand for sensor-based load forecasting. Conventional ML allows training a
single global model using data from multiple smart meters requiring data
transfer to a central server, raising concerns for network requirements,
privacy, and security. We propose a split learning-based framework for load
forecasting to alleviate this issue. We split a deep neural network model into
two parts, one for each Grid Station (GS) responsible for an entire
neighbourhood's smart meters and the other for the Service Provider (SP).
Instead of sharing their data, client smart meters use their respective GSs'
model split for forward pass and only share their activations with the GS.
Under this framework, each GS is responsible for training a personalized model
split for their respective neighbourhoods, whereas the SP can train a single
global or personalized model for each GS. Experiments show that the proposed
models match or exceed a centrally trained model's performance and generalize
well. Privacy is analyzed by assessing information leakage between data and
shared activations of the GS model split. Additionally, differential privacy
enhances local data privacy while examining its impact on performance. A
transformer model is used as our base learner.
