  Federated Learning (FL) has demonstrated a significant potential to improve
the quality of service (QoS) of EV charging stations. While existing studies
have primarily focused on developing FL algorithms, the effect of FL on the
charging stations' operation in terms of price competition has yet to be fully
understood. This paper aims to fill this gap by modeling the strategic
interactions between two charging stations and EV owners as a multi-stage game.
Each station first decides its FL participation strategy and charging price,
and then individual EV owners decide their charging strategies. The game
analysis involves solving a non-concave problem and by decomposing it into a
piece-wise concave program we manage to fully characterize the equilibrium.
Based on real-world datasets, our numerical results reveal an interesting
insight: even if FL improves QoS, it can lead to smaller profits for both
stations. The key reason is that FL intensifies the price competition between
charging stations by improving stations' QoS to a similar level. We further
show that the stations will participate in FL when their data distributions are
mildly dissimilar.
