To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithm (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for machine translation tasks due to the vast communication overheads and heavy synchronization. In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework that, instead of multi-round model-based interactions, leverages one-round memorization-based interaction to share knowledge across different clients to build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a $k$-nearest-neighbor ($$kNN) classifier and integrates the external datastore constructed by private text data in all clients to form the final FL model. A two-phase datastore encryption strategy is introduced to achieve privacy-preserving during this process. Extensive experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg, while maintaining promising performance in different FL settings.

翻译：为保护用户隐私并满足法律法规要求，联邦学习（FL）正受到广泛关注。采用传统联邦学习算法（如FedAvg）训练神经机器翻译（NMT）模型通常依赖于多轮基于模型的交互。然而，由于巨大的通信开销和繁重的同步负担，这种方法在机器翻译任务中既不实际也不高效。本文提出了一种新颖的联邦最近邻（FedNN）机器翻译框架，该框架摒弃了多轮基于模型的交互，转而利用单轮基于记忆的交互在不同客户端之间共享知识，从而构建低开销的隐私保护系统。该方法为在大规模可访问数据上训练的公开NMT模型配备了$k$-最近邻（$k$NN）分类器，并整合了所有客户端私有文本数据构建的外部数据存储，形成最终的联邦学习模型。在此过程中引入了一种两阶段数据存储加密策略，以实现隐私保护。大量实验表明，与FedAvg相比，FedNN在显著降低计算和通信成本的同时，在不同联邦学习设置下仍保持了优秀的性能表现。