Summary of On the Convergence Of Zeroth-order Federated Tuning For Large Language Models, by Zhenqing Ling et al.
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
by Zhenqing Ling, Daoyuan Chen, Liuyi Yao, Yaliang Li, Ying Shen
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory requirements for fine-tuning LLMs pose significant challenges, especially when deploying on clients with limited computational resources. To circumvent this, we explore the novel integration of Memory-efficient Zeroth-Order Optimization within a federated setting, a synergy we term as FedMeZO. Our study is the first to examine the theoretical underpinnings of FedMeZO in the context of LLMs, tackling key questions regarding the influence of large parameter spaces on optimization behavior, the establishment of convergence properties, and the identification of critical parameters for convergence to inform personalized federated strategies. FedMeZO converges faster than traditional first-order methods such as FedAvg while significantly reducing GPU memory usage during training. Moreover, a proposed personalized FL strategy built upon theoretical insights customizes client-wise learning rates to accelerate loss reduction. The paper’s findings contribute to bridging the gap between theoretical and practical aspects of federated fine-tuning for LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a new way to train artificial intelligence models without sharing private data. Large Language Models are super powerful computers that can understand human language, but they need lots of computer power to learn. A team of researchers found a way to make these models work better with less computer power by combining two ideas: Federated Learning and Memory-efficient Zeroth-Order Optimization. This new approach is called FedMeZO. The scientists studied how well FedMeZO works for Large Language Models, and they found that it trains faster and uses less computer memory than usual methods. The team hopes their work will help make it easier to train these powerful models without needing lots of powerful computers. |
Keywords
* Artificial intelligence * Federated learning * Fine tuning * Natural language processing * Optimization
