Summary of Cg-fedllm: How to Compress Gradients in Federated Fune-tuning For Large Language Models, by Huiwen Wu et al.
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
by Huiwen Wu, Xiaohan Li, Deyi Zhang, Xiaogang Xu, Jiafei Wu, Puning Zhao, Zhe Liu
First submitted to arxiv on: 22 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 The proposed approach, CG-FedLLM, tackles the communication costs issue in Federated Learning (FL) for Large-Language Models (LLMs). It integrates an encoder on clients to compress gradients and a decoder on the server to reconstruct them. The new pipeline also includes Temporal-ensemble Gradient-Aware Pre-training (TGAP) and Federated AutoEncoder-Involved Fine-tuning (FAF) for efficient gradient compression. Experiments show that this approach reduces communication costs and improves performance, achieving an average 3-point increment compared to traditional methods on the C-Eval benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very powerful tools that can understand and generate human-like text. However, they require a lot of data to learn from, which can be a problem because it’s sensitive information. One way to solve this issue is by using something called Federated Learning. This method allows many devices or computers to work together on a task without sharing their raw data. The problem is that these large language models have so many parameters that they require a lot of communication between the devices, which can be slow and inefficient. To fix this, researchers developed an innovative way to compress the gradients (the changes in the model’s weights) to reduce the amount of data needed for communication. This new approach uses an encoder on each device to shrink the gradients and a decoder on the server to expand them back to their original size. They also came up with two new training strategies: Temporal-ensemble Gradient-Aware Pre-training (TGAP) and Federated AutoEncoder-Involved Fine-tuning (FAF). By using these strategies, they were able to reduce communication costs and improve performance. |
Keywords
» Artificial intelligence » Autoencoder » Decoder » Encoder » Federated learning » Fine tuning
