Summary of The Future Of Large Language Model Pre-training Is Federated, by Lorenzo Sani et al.
The Future of Large Language Model Pre-training is Federated
by Lorenzo Sani, Alex Iacob, Zeyu Cao, Bill Marino, Yan Gao, Tomas Paulik, Wanru Zhao, William F. Shen, Preslav Aleksandrov, Xinchi Qiu, Nicholas D. Lane
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Generative large language models (LLMs) have achieved impressive results across various tasks, thanks to their massive training datasets. To continue improving performance, future LLMs will require increased computing and data resources, which can be unlocked through federated learning (FL). Our work presents a robust FL approach, called Photon, enabling large-scale collaboration for pre-training LLMs with billions of parameters. We demonstrate Photon’s effectiveness in mobilizing more resources while matching centralized performance. Our experiments show that the approach scales well with model size and enables training billion-scale federated LLMs using limited resources. Additionally, we highlight Photon’s resilience to classical FL challenges, such as hardware heterogeneity and partial participation. This innovation empowers data-rich actors to participate in LLM pre-training, rather than relying solely on compute-rich actors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to make computers learn even better by combining the data and computing power from many places around the world. That’s what this paper is all about! The authors propose a new method called Photon that lets organizations work together to train massive language models, even with limited resources. They show that this approach can be very effective and robust against common problems. This means that more people and organizations can contribute to making these language models better, without having to rely on just one powerful computer or data center. |
Keywords
» Artificial intelligence » Federated learning
