Summary of Mira: a Method Of Federated Multi-task Learning For Large Language Models, by Ahmed Elbakary et al.
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models
by Ahmed Elbakary, Chaouki Ben Issaid, Tamer ElBatt, Karim Seddik, Mehdi Bennis
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper introduces a novel approach for fine-tuning Large Language Models (LLMs) inspired by Multi-Task learning in a federated manner. It proposes a method that leverages the structure of each client’s model and enables a learning scheme that considers other clients’ tasks and data distribution. The authors utilize Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, to reduce the number of trainable parameters. Experimental results show that the proposed method outperforms existing frameworks for federated fine-tuning of LLMs in terms of average and local performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make large language models work better together by sharing information between different devices or tasks. It uses a special way of fine-tuning these models called LoRA, which helps reduce the amount of calculations needed. The results show that this new approach works well for fine-tuning language models in a federated setting. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation » Multi task » Parameter efficient
