  Federated Learning is an emerging privacy-preserving distributed machine
learning approach to building a shared model by performing distributed training
locally on participating devices (clients) and aggregating the local models
into a global one. As this approach prevents data collection and aggregation,
it helps in reducing associated privacy risks to a great extent. However, the
data samples across all participating clients are usually not independent and
identically distributed (non-iid), and Out of Distribution(OOD) generalization
for the learned models can be poor. Besides this challenge, federated learning
also remains vulnerable to various 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 for learning invariant (causal)
features common to all participating clients in a federated learning setup and
analyze empirically how it enhances the Out of Distribution (OOD) accuracy as
well as the privacy of the final learned model.
