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.

提出一种新的联邦自然语言生成（FedNLG）框架，该框架从分布式设备上学习个性化表示，并以高效和安全的方式实现个性化对话系统，该模型首先在大型对话语料库上预训练标准神经会话模型的参数，然后以联邦方式在特定数据集上微调模型参数和人物嵌入，从而同时在本地客户端上学习人格嵌入并通过联邦聚合学习共享模型参数，实现准确性和隐私权的平衡。

面向个性化对话系统的联邦自然语言生成