Federated Nearest Neighbor Machine Translation

• URL: http://arxiv.org/abs/2302.12211v1
• Date: Thu, 23 Feb 2023 18:04:07 GMT
• Title: Federated Nearest Neighbor Machine Translation
• Authors: Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu and Enhong Chen
• Abstract summary: In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework. FedNN leverages one-round memorization-based interaction to share knowledge across different clients. Experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg.
• Score: 66.8765098651988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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