Federated Nearest Neighbor Machine Translation

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithms (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for 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 and build low-overhead privacy-preserving systems. The whole approach equips the public NMT model trained on large-scale accessible data with a k-nearestneighbor (kNN) classifier and integrates the external datastore constructed by private text data from 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 translation performance in different FL settings. In recent years, neural machine translation (NMT) has significantly improved translation quality (Bahdanau et al., 2015; Vaswani et al., 2017; Hassan et al., 2018) and has been widely adopted in many commercial systems. The current mainstream system is first built on a large-scale corpus collected by the service provider and then directly applied to translation tasks for different users and enterprises. However, this application paradigm faces two critical challenges in practice.