  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.
