Summary of One Communication Round Is All It Needs For Federated Fine-tuning Foundation Models, by Ziyao Wang et al.
One Communication Round is All It Needs for Federated Fine-Tuning Foundation Models
by Ziyao Wang, Bowei Tian, Yexiao He, Zheyu Shen, Luyang Liu, Ang Li
First submitted to arxiv on: 5 Dec 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 presents a groundbreaking discovery that challenges the conventional wisdom on federated fine-tuning large foundation models (FMs). It reveals that traditional multi-round aggregation algorithms may not be necessary, instead, a single round of communication can achieve comparable global model performance. The researchers demonstrate this through theoretical and empirical analyses, showing that large FMs with extensive parameter sizes and pre-training on general tasks can reduce training loss in one-shot federated fine-tuning compared to smaller models. The experiments also show that one-shot federated fine-tuning reduces communication costs, enables asynchronous aggregation, enhances privacy, and maintains performance consistency for models larger than 1 billion parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated fine-tuning is a way to train artificial intelligence models on lots of different data without sharing the data itself. It’s like training multiple models at once, but instead of sending all the data between devices, each device only sends the parts it needs to learn. The problem is that big AI models need a lot of communication to fine-tune them, which can be slow and expensive. But this paper shows that you don’t always need to do multiple rounds of communication. Sometimes, just one round is enough! This makes training bigger AI models faster, cheaper, and more private. |
Keywords
» Artificial intelligence » Fine tuning » One shot
