Summary of Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation, by Yun-wei Chu et al.
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
by Yun-Wei Chu, Dong-Jun Han, Christopher G. Brinton
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to federated learning (FL) for multilingual neural machine translation (NMT). The main challenge is efficient transmission of large-scale NMT engines between FL parties, which is addressed by introducing MetaSend, a meta-learning-based adaptive parameter selection methodology. This method learns a dynamic threshold to filter parameters prior to transmission, ensuring high-quality translations despite limited communication budgets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn new languages better! It’s about using computers to translate languages, but with many people having their own language data, it gets tricky to share all the information. The researchers came up with a clever way to send only the important parts of the translation model between groups working together. They tested this idea on two big datasets and showed that it works really well, even when there’s limited bandwidth. |
Keywords
» Artificial intelligence » Federated learning » Meta learning » Translation
