Summary of Openfedllm: Training Large Language Models on Decentralized Private Data Via Federated Learning, by Rui Ye et al.
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
by Rui Ye, Wenhao Wang, Jingyi Chai, Dihan Li, Zexi Li, Yinda Xu, Yaxin Du, Yanfeng Wang, Siheng Chen
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
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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 paper presents a solution for training large language models (LLMs) on private data while preserving privacy, using federated learning (FL). The authors introduce OpenFedLLM, a framework and codebase that enables collaborative LLM training without sharing raw data. The framework includes FL algorithms, domain adaptation, and comprehensive evaluations across various datasets and metrics. Experiments demonstrate the effectiveness of FL algorithms in outperforming local training, with significant improvements in financial benchmark results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to train language models using private data while keeping it safe. Right now, big language models are trained on lots of publicly available data, but soon there won’t be enough. The authors suggest a new method called federated learning (FL), where multiple people or organizations work together to train a model without sharing their individual data. They created a special codebase and framework called OpenFedLLM that makes this possible. It includes ways to fine-tune the model, align it with human values, and test its performance on different tasks. The results show that FL can improve the performance of language models, especially in financial applications. |
Keywords
* Artificial intelligence * Domain adaptation * Federated learning
