Summary of Federated Learning Driven Large Language Models For Swarm Intelligence: a Survey, by Youyang Qu
Federated Learning driven Large Language Models for Swarm Intelligence: A Survey
by Youyang Qu
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 Federated learning (FL) enables training large language models (LLMs) while addressing data privacy and decentralization challenges. This paper surveys recent advancements in federated LLM training, focusing on machine unlearning to comply with regulations like the Right to be Forgotten. Machine unlearning involves securely removing individual data contributions from the learned model without retraining from scratch. Strategies include perturbation techniques, model decomposition, and incremental learning, which maintain model performance and data privacy. Case studies and experimental results demonstrate the effectiveness and efficiency of these approaches in real-world scenarios. The survey reveals a growing interest in developing robust and scalable federated unlearning methods, highlighting the importance of AI ethics and distributed machine learning technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about training big language models without sharing personal data. It’s like having a big library where many people contribute books, but nobody can see what’s inside each book. The model learns from all these books, but it can’t be used to identify any individual person. This technology is important because it helps keep our privacy safe in the digital age. The paper talks about how this works and shows some examples of how well it does. It’s a big area for research and development, especially when it comes to making sure AI technology respects people’s privacy. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
