  Driving etiquette takes a different flavor for each locality as drivers not
only comply with rules/laws but also abide by local unspoken convention. When
to have the turn signal (indicator) on/off is one such etiquette which does not
have a definitive right or wrong answer. Learning this behavior from the
abundance of data generated from various sensor modalities integrated in the
vehicle is a suitable candidate for deep learning. But what makes it a prime
candidate for Federated Learning are privacy concerns and bandwidth limitations
for any data aggregation. This paper presents a long short-term memory (LSTM)
based Turn Signal Prediction (on or off) model using vehicle control area
network (CAN) signal data. The model is trained using two approaches, one by
centrally aggregating the data and the other in a federated manner. Centrally
trained models and federated models are compared under similar hyperparameter
settings. This research demonstrates the efficacy of federated learning, paving
the way for in-vehicle learning of driving etiquette.
