Summary of Probing the Efficacy Of Federated Parameter-efficient Fine-tuning Of Vision Transformers For Medical Image Classification, by Naif Alkhunaizi et al.
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification
by Naif Alkhunaizi, Faris Almalik, Rouqaiah Al-Refai, Muzammal Naseer, Karthik Nandakumar
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The paper investigates various algorithms for fine-tuning pre-trained Vision Transformer (ViT) models on medical imaging tasks, considering the challenges of limited training data, data silos, and privacy constraints. The authors introduce new federated variants of parameter-efficient fine-tuning (PEFT) methods, including visual prompt tuning, low-rank decomposition, stochastic block attention fine-tuning, and hybrid PEFT methods. They perform a thorough empirical analysis to identify the optimal PEFT method for the federated setting and study the impact of data distribution on federated PEFT, particularly in out-of-domain (OOD) and non-IID scenarios. The results show that while most federated PEFT methods work well for in-domain transfer, there is a trade-off between accuracy and efficiency when dealing with OOD and non-IID data, emphasizing the importance of selecting the initial model wisely. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to fine-tune pre-trained models on medical images. It’s trying to solve a problem where we don’t have enough training data or it’s private, so we need to find efficient ways to adapt these models without sharing all their information. The authors develop new methods to do this and test them to see which one works best in different situations. They found that most of the methods work well when they’re used on similar medical images, but if the images are very different, there’s a trade-off between how accurate the model is and how efficient it is. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Parameter efficient » Prompt » Vision transformer » Vit
