The task of sentiment analysis has been extensively studied in high-resource languages. Even though sentiment analysis is studied for some resource-constrained languages, the corpora and the datasets available in other low resource languages are scarce and fragmented. This prevents further research of resource-constrained languages and also inhibits model performance for these languages. Privacy concerns may also be raised while aggregating some datasets for training central models. Our work tries to steer the research of sentiment analysis for resource-constrained languages in the direction of Federated Learning. We conduct various experiments to compare server based and federated approaches for 4 Indic Languages - Marathi, Hindi, Bengali, and Telugu. Specifically, we show that a privacy preserving approach, Federated Learning surpasses traditional server trained LSTM model and exhibits comparable performance to other servers-side transformer models.