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Federated learning is a training paradigm that learns from multipledistributed users without aggregating data on a centralized server. Such aparadigm promises the ability to deploy machine-learning at-scale to a diversepopulation of end-users without first collecting a large, labeled dataset forall possible tasks. As federated learning typically averages learning updatesacross a decentralized population, there is a growing need for personalizationof federated learning systems (i.e conversational agents must be able topersonalize to a specific user's preferences). In this work, we propose a newdirection for personalization research within federated learning, leveragingboth personal embeddings and shared context embeddings. We also present anapproach to predict these ``preference'' embeddings, enabling personalizationwithout backpropagation. Compared to state-of-the-art personalizationbaselines, our approach achieves a 50\% improvement in test-time perplexityusing 0.001\% of the memory required by baseline approaches, and achievinggreater sample- and compute-efficiency.

FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings