  Machine learning (ML) applications to time series energy utilization
forecasting problems are a challenging assignment due to a variety of factors.
Chief among these is the non-homogeneity of the energy utilization datasets and
the geographical dispersion of energy consumers. Furthermore, these ML models
require vast amounts of training data and communications overhead in order to
develop an effective model. In this paper, we propose a communication-efficient
time series forecasting model combining the most recent advancements in
transformer architectures implemented across a geographically dispersed series
of EV charging stations and an efficient variant of federated learning (FL) to
enable distributed training. The time series prediction performance and
communication overhead cost of our FL are compared against their counterpart
models and shown to have parity in performance while consuming significantly
lower data rates during training. Additionally, the comparison is made across
EV charging as well as other time series datasets to demonstrate the
flexibility of our proposed model in generalized time series prediction beyond
energy demand. The source code for this work is available at
https://github.com/XuJiacong/LoGTST_PSGF
