Summary of Personalized Wireless Federated Learning For Large Language Models, by Feibo Jiang et al.
Personalized Wireless Federated Learning for Large Language Models
by Feibo Jiang, Li Dong, Siwei Tu, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Dusit Niyato
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have transformed natural language processing tasks, but their deployment in wireless networks faces challenges regarding privacy and security protection mechanisms. Federated Learning (FL) has emerged as a promising approach to address these challenges, although it suffers from inefficiencies handling big and heterogeneous data, resource-intensive training, and high communication overhead. To tackle these issues, this paper compares different learning stages and features of LLMs in wireless networks. It then introduces two personalized wireless federated fine-tuning methods: Personalized Federated Instruction Tuning (PFIT) using reinforcement learning to fine-tune local LLMs with diverse reward models; and Personalized Federated Task Tuning (PFTT), which leverages global adapters and local Low-Rank Adaptations (LoRA) for collaborative fine-tuning. The paper performs simulations demonstrating the effectiveness of these methods and discusses open issues. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using special models called Large Language Models in wireless networks, like our phones and internet. Right now, there’s a problem with keeping our data safe when we use these models online. To fix this, researchers are trying to develop new ways to make these models work better together. They compared different methods and came up with two new ideas: one uses rewards to fine-tune the models, and another uses special adaptations to help them work together. The paper tested these ideas and shows that they can be helpful in keeping our data safe. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Instruction tuning » Lora » Natural language processing » Reinforcement learning