Summary of Fisc: Federated Domain Generalization Via Interpolative Style Transfer and Contrastive Learning, by Dung Thuy Nguyen et al.
FISC: Federated Domain Generalization via Interpolative Style Transfer and Contrastive Learning
by Dung Thuy Nguyen, Taylor T. Johnson, Kevin Leach
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 explores the challenges of Federated Learning (FL) when dealing with diverse client domains. Current FL solutions focus on private data from a single domain, but this approach fails to handle domain shift, leading to poor performance on unseen domains. The authors propose new approaches to address Federated Domain Generalization, acknowledging limitations in existing methods that assume each client holds data for an entire domain. By leveraging domain-based heterogeneity and client sampling, the proposed solutions aim to improve FL’s adaptability to real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for different devices or computers to learn together without sharing their personal information. Right now, most of these learning methods focus on data from one specific group or community. However, what happens when we have many groups with different types of data? This can cause problems because the models might not work well in new, unseen groups. The authors of this paper are trying to solve this issue by developing new ways for Federated Learning to adapt to different groups and improve performance. |
Keywords
» Artificial intelligence » Domain generalization » Federated learning
