Summary of Efedllm: Efficient Llm Inference Based on Federated Learning, by Shengwen Ding and Chenhui Hu
eFedLLM: Efficient LLM Inference Based on Federated Learning
by Shengwen Ding, Chenhui Hu
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 enhances the operational efficiency and affordability of Large Language Model (LLM) inference by utilizing transformer-based federated learning with model-parallel distributed training. This allows for the distribution of computational loads and memory requirements across a network, making it possible for users to collaboratively train state-of-the-art LLMs, even with limited resources. The approach also includes an incentive mechanism that rewards constructive contributions and filters out malicious activities, ensuring the integrity and reliability of the training process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are revolutionizing artificial intelligence, but they require a lot of computing power and memory to work effectively. This makes it hard for many people to use them. To fix this problem, researchers have come up with a new way to train these models using a method called federated learning. This allows multiple people to contribute to the training process, even if they don’t have powerful computers. The approach also includes a system to reward helpful contributions and keep out bad ones, making sure the results are trustworthy. |
Keywords
» Artificial intelligence » Federated learning » Inference » Large language model » Transformer
