  Neural conversational models have long suffered from the problem of
inconsistency and lacking coherent personality. To address the issue,
persona-based models capturing individual characteristics have been proposed,
but they still face the dilemma of model adaption and data privacy. To break
this dilemma, we propose a novel Federated Natural Language Generation (FedNLG)
framework, which learns personalized representations from various dataset on
distributed devices, and thus implements the personalized dialogue system
efficiently and safely. FedNLG first pre-trains parameters of standard neural
conversational model over a large dialogue corpus, and then fine-tune the model
parameters and persona embeddings on specific datasets, in a federated manner.
Thus, the model could simultaneously learn the persona embeddings in local
clients and learn shared model parameters by federated aggregation, which
achieves accuracyprivacy balance. By conducting extensive experiments, we
demonstrate the effectiveness of our model by pre-training model over Cornell
Movie-Dialogs Corpus and fine-tuning the model over two TV series dataset.
