  Federated Learning (FL) is quickly becoming a goto distributed training
paradigm for users to jointly train a global model without physically sharing
their data. Users can indirectly contribute to, and directly benefit from a
much larger aggregate data corpus used to train the global model. However,
literature on successful application of FL in real-world problem settings is
somewhat sparse. In this paper, we describe our experience applying a FL based
solution to the Named Entity Recognition (NER) task for an adverse event
detection application in the context of mass scale vaccination programs. We
present a comprehensive empirical analysis of various dimensions of benefits
gained with FL based training. Furthermore, we investigate effects of tighter
Differential Privacy (DP) constraints in highly sensitive settings where
federation users must enforce Local DP to ensure strict privacy guarantees. We
show that local DP can severely cripple the global model's prediction accuracy,
thus dis-incentivizing users from participating in the federation. In response,
we demonstrate how recent innovation on personalization methods can help
significantly recover the lost accuracy. We focus our analysis on the Federated
Fine-Tuning algorithm, FedFT, and prove that it is not PAC Identifiable, thus
making it even more attractive for FL-based training.
