Summary of Fl-tac: Enhanced Fine-tuning in Federated Learning Via Low-rank, Task-specific Adapter Clustering, by Siqi Ping et al.
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
by Siqi Ping, Yuzhu Mao, Yang Liu, Xiao-Ping Zhang, Wenbo Ding
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers tackle the challenge of fine-tuning large-scale pre-trained models for downstream tasks using Federated Learning (FL). While FL enables model adaptation across various clients with diverse data, it is hindered by significant communication overhead due to the massive size of these pre-trained models. The authors propose a solution called low-rank fine-tuning, which involves training a low-rank adapter on each client and then clustering similar adapters at the server side to achieve task-specific aggregation. The proposed method, dubbed Low-Rank Task-Specific Adapter Clustering (TAC), is evaluated on various language and vision tasks, including GLUE and CIFAR-10/100. The results show the evolution of task-specific adapters throughout the FL training process and confirm the effectiveness of the TAC approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in artificial intelligence. When we want to use a big pre-trained model for a new task, it’s hard to get enough good data to make it work well. Federated Learning is an idea that can help by letting many devices share their own small amounts of data. But this can be slow because the models are so big. The researchers came up with a clever solution called low-rank fine-tuning. They train a smaller adapter for each task and then group similar adapters together to make it faster. They tested this idea on lots of different tasks, like language and image recognition, and showed that it really works well. |
Keywords
» Artificial intelligence » Clustering » Federated learning » Fine tuning
