Federated learning enables participating entities to collaboratively learn a shared prediction model while keeping their training data local. As this technique prevents data collection and aggregation, it helps in deducting associated privacy risks to a great extent. However, it still remains vulnerable to numerous attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach to learn causal features common to all participating clients in a federated learning setup and analyse how it enhances the out of distribution accuracy and privacy.