Summary of Feddiff: Diffusion Model Driven Federated Learning For Multi-modal and Multi-clients, by Daixun Li et al.
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
by DaiXun Li, Weiying Xie, ZiXuan Wang, YiBing Lu, Yunsong Li, Leyuan Fang
First submitted to arxiv on: 16 Nov 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 propose FedDiff, a novel framework for multi-modal remote sensing data fusion. The framework enables secure fusion of heterogeneous data from multiple clients by establishing a dual-branch diffusion model feature extraction setup. This setup allows for complementary denoising steps between different modalities, such as hyperspectral and LiDAR data. To facilitate private and efficient communication between clients, the framework embeds the diffusion model into a federated learning structure with a lightweight communication module. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have different cameras that take pictures of the same thing from different angles. This paper helps figure out how to combine those pictures so they look really good and make sense together. They created a special way to do this called FedDiff, which makes sure that all the information stays private and secure. It’s like taking a bunch of puzzle pieces from different cameras and putting them together into one complete picture. |
Keywords
* Artificial intelligence * Diffusion model * Feature extraction * Federated learning * Multi modal
