  This paper studies a new task of federated learning (FL) for semantic
parsing, where multiple clients collaboratively train one global model without
sharing their semantic parsing data. By leveraging data from multiple clients,
the FL paradigm can be especially beneficial for clients that have little
training data to develop a data-hungry neural semantic parser on their own. We
propose an evaluation setup to study this task, where we re-purpose widely-used
single-domain text-to-SQL datasets as clients to form a realistic heterogeneous
FL setting and collaboratively train a global model. As standard FL algorithms
suffer from the high client heterogeneity in our realistic setup, we further
propose a novel LOss Reduction Adjusted Re-weighting (Lorar) mechanism to
mitigate the performance degradation, which adjusts each client's contribution
to the global model update based on its training loss reduction during each
round. Our intuition is that the larger the loss reduction, the further away
the current global model is from the client's local optimum, and the larger
weight the client should get. By applying Lorar to three widely adopted FL
algorithms (FedAvg, FedOPT and FedProx), we observe that their performance can
be improved substantially on average (4%-20% absolute gain under MacroAvg) and
that clients with smaller datasets enjoy larger performance gains. In addition,
the global model converges faster for almost all the clients.
