Summary of Cellm: An Efficient Communication in Large Language Models Training For Federated Learning, by Raja Vavekanand et al.
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning
by Raja Vavekanand, Kira Sam
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper proposes a novel approach to Federated Learning (FL) that addresses the challenges of statistical heterogeneity in client devices’ local data distributions. By employing Large Language Models (LLMs), which can learn from vast amounts of noisy data, the authors aim to develop efficient training methods for LLMs in FL. To achieve this, they employ two techniques: low-rank adaptation (LoRA) to reduce computational load and sparse updates throughout training to minimize communication costs. The proposed method demonstrates significant improvements over existing baselines, reducing communication costs by up to 10x while achieving greater utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a type of artificial intelligence called Federated Learning more efficient. Right now, it’s hard for different devices to work together because their data is different and can’t be shared directly. This makes it hard to train models that work well on all the data. Large Language Models are special models that are good at learning from noisy data, which could help solve this problem. The authors of this paper found ways to make training these models more efficient by reducing how much computing is needed and minimizing how much data has to be shared between devices. This makes it possible for devices to work together better and create even better models. |
Keywords
» Artificial intelligence » Federated learning » Lora » Low rank adaptation
