  Smart energy performance monitoring and optimisation at the supplier and
consumer levels is essential to realising smart cities. In order to implement a
more sustainable energy management plan, it is crucial to conduct a better
energy forecast. The next-generation smart meters can also be used to measure,
record, and report energy consumption data, which can be used to train machine
learning (ML) models for predicting energy needs. However, sharing fine-grained
energy data and performing centralised learning may compromise users' privacy
and leave them vulnerable to several attacks. This study addresses this issue
by utilising federated learning (FL), an emerging technique that performs ML
model training at the user level, where data resides. We introduce FedTrees, a
new, lightweight FL framework that benefits from the outstanding features of
ensemble learning. Furthermore, we developed a delta-based early stopping
algorithm to monitor FL training and stop it when it does not need to continue.
The simulation results demonstrate that FedTrees outperforms the most popular
federated averaging (FedAvg) framework and the baseline Persistence model for
providing accurate energy forecasting patterns while taking only 2% of the
computation time and 13% of the communication rounds compared to FedAvg, saving
considerable amounts of computation and communication resources.
