Summary of Navigating Heterogeneity and Privacy in One-shot Federated Learning with Diffusion Models, by Matias Mendieta et al.
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models
by Matias Mendieta, Guangyu Sun, Chen Chen
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); 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 The paper investigates the application of diffusion models in one-shot federated learning (FL), addressing data heterogeneity and improving FL performance. One-shot FL reduces communication rounds, improves efficiency, and provides better security against eavesdropping attacks. The authors demonstrate the effectiveness of their proposed diffusion model approach, FedDiff, in tackling data heterogeneity and achieving better FL results. Additionally, they evaluate the utility of FedDiff compared to other one-shot FL methods under differential privacy (DP) settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers explore how to improve federated learning by using a special kind of machine learning model called a diffusion model. Federated learning is a way for many devices or computers to work together to train a single model without sharing their individual data. The problem with current methods is that they can be slow and don’t handle different types of data well. This paper shows how the diffusion model can help fix these issues, making federated learning more efficient and secure. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Federated learning » Machine learning » One shot
