Summary of Towards Multi-modal Transformers in Federated Learning, by Guangyu Sun et al.
Towards Multi-modal Transformers in Federated Learning
by Guangyu Sun, Matias Mendieta, Aritra Dutta, Xin Li, Chen Chen
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper addresses a crucial issue in the development of multi-modal transformers: the lack of high-quality data from diverse domains. Federated learning (FL) has emerged as a promising approach to train models without direct access to raw data, but existing methods have limitations when dealing with unpaired uni-modal clients and transformer architectures. This study explores transfer multi-modal federated learning (MFL) in the vision-language domain, where clients possess data from different modalities distributed across various datasets. The authors evaluate the performance of existing methods using a transformer architecture and introduce a novel framework called Federated modality complementary and collaboration (FedCola), which addresses gaps among clients. FedCola demonstrates superior performance over previous approaches through extensive experiments in various FL settings, offering new perspectives on future federated training of multi-modal transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem with developing special kinds of computer models called multi-modal transformers. These models are really good at working with different types of data like pictures and words, but they need lots of high-quality data to get even better. One way to get this data is by using something called federated learning (FL), which lets different computers share their own small pieces of data without sharing all the details. The problem is that existing methods aren’t very good at working with computers that have different types of data, like pictures or words. This study explores a new way to do FL that works better when dealing with these different types of data. The authors test this new method and show it’s actually much better than what we had before. |
Keywords
* Artificial intelligence * Federated learning * Multi modal * Transformer
