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Federated Learning is an emerging privacy-preserving distributed machinelearning approach to building a shared model by performing distributed traininglocally on participating devices (clients) and aggregating the local modelsinto a global one. As this approach prevents data collection and aggregation,it helps in reducing associated privacy risks to a great extent. However, thedata samples across all participating clients are usually not independent andidentically distributed (non-iid), and Out of Distribution(OOD) generalizationfor the learned models can be poor. Besides this challenge, federated learningalso remains vulnerable to various attacks on security wherein a few maliciousparticipating entities work towards inserting backdoors, degrading thegenerated aggregated model as well as inferring the data owned by participatingentities. In this paper, we propose an approach for learning invariant (causal)features common to all participating clients in a federated learning setup andanalyze empirically how it enhances the Out of Distribution (OOD) accuracy aswell as the privacy of the final learned model.

Towards Causal Federated Learning For Enhanced Robustness and Privacy