Summary of Worldwide Federated Training Of Language Models, by Alex Iacob and Lorenzo Sani and Bill Marino and Preslav Aleksandrov and William F. Shen and Nicholas Donald Lane
Worldwide Federated Training of Language Models
by Alex Iacob, Lorenzo Sani, Bill Marino, Preslav Aleksandrov, William F. Shen, Nicholas Donald Lane
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 The proposed Worldwide Federated Language Model Training (WorldLM) system addresses the limitations of traditional language model training by enabling collaboration across heterogeneous legal, security, and privacy regimes. This system, based on federations of federations, allows each federation to account for factors such as industry, operating jurisdiction, or competitive environment while adapting to statistical heterogeneity through partial model localization. WorldLM also enables adaptive information sharing across federations via residual layer embeddings, outperforming standard federations by up to 1.91 times and approaching personalized performance of fully local models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary World Federated Language Model Training is a new way to train language models that works better than the old way because it lets different groups work together even if they have different rules and regulations. This system, called WorldLM, makes sure each group can use its own data and adapt to its own needs while still sharing some information with other groups. This helps create more accurate language models and keeps them private. |
Keywords
» Artificial intelligence » Language model
