Summary of Federated In-context Llm Agent Learning, by Panlong Wu et al.
Federated In-Context LLM Agent Learning
by Panlong Wu, Kangshuo Li, Junbao Nan, Fangxin Wang
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for training Large Language Models (LLMs) in a federated learning setting, while preserving the privacy of sensitive data. The authors suggest using an “in-context learning” capability, where language is aggregated rather than model parameters. However, this method risks privacy leakage and requires the collection and presentation of data samples from various clients during aggregation. To mitigate these issues, the paper introduces a Federated In-Context LLM Agent Learning (FICAL) algorithm that uses knowledge compendiums generated by an enhanced Knowledge Compendiums Generation (KCG) module instead of model parameters. The authors also design a Retrieval Augmented Generation (RAG) based Tool Learning and Utilizing (TLU) module, which incorporates the aggregated global knowledge compendium as a teacher to teach LLM agents the usage of tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to train Language Models while keeping private data safe. The approach uses “in-context learning” where language is combined instead of model details. However, this method may reveal sensitive information and needs to collect and share data from different sources. To fix these problems, the authors create an algorithm called FICAL that replaces model details with special knowledge containers. They also design a tool-learning system that uses global knowledge to teach models how to use tools. |
Keywords
» Artificial intelligence » Federated learning » Rag » Retrieval augmented generation
