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Federated learning (FL) is a promising approach for solving multilingualtasks, potentially enabling clients with their own language-specific data tocollaboratively construct a high-quality neural machine translation (NMT)model. However, communication constraints in practical network systems presentchallenges for exchanging large-scale NMT engines between FL parties. In thispaper, we propose a meta-learning-based adaptive parameter selectionmethodology, MetaSend, that improves the communication efficiency of modeltransmissions from clients during FL-based multilingual NMT training. Ourapproach learns a dynamic threshold for filtering parameters prior totransmission without compromising the NMT model quality, based on the tensordeviations of clients between different FL rounds. Through experiments on twoNMT datasets with different language distributions, we demonstrate thatMetaSend obtains substantial improvements over baselines in translation qualityin the presence of a limited communication budget.

Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation