Summary of Fedmllm: Federated Fine-tuning Mllm on Multimodal Heterogeneity Data, by Binqian Xu et al.
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
by Binqian Xu, Xiangbo Shu, Haiyang Mei, Guosen Xie, Basura Fernando, Jinhui Tang
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 introduce a benchmark for evaluating the performance of fine-tuned Multimodal Large Language Models (MLLMs) across various scenarios, aiming to address the challenges posed by multimodal heterogeneities. They propose a Federated Learning (FL) framework that integrates classic FL methods with modality-agnostic strategies to improve MLLM performance in privacy-sensitive domains. The benchmark includes two lightweight MLLMs, two downstream tasks, three evaluation metrics, and five datasets across three domains, as well as six comparison baselines covering over ten types of modality heterogeneities across four multimodal scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better by combining different kinds of data. It’s like trying to understand a mix of pictures, sounds, and words all at once! The researchers are working on a new way to train these models using small amounts of private data from different sources, so they can be used in places where privacy matters. They also want to figure out how to make the models work better when they’re given very different kinds of information. |
Keywords
* Artificial intelligence * Federated learning
