Summary of A Comprehensive Survey Of Federated Transfer Learning: Challenges, Methods and Applications, by Wei Guo et al.
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
by Wei Guo, Fuzhen Zhuang, Xiao Zhang, Yiqi Tong, Jin Dong
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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 This paper discusses the challenges of Federated Learning (FL), a distributed machine learning paradigm that enables participants to collaborate without sharing their data. Many FL methods struggle due to differences in feature spaces and distributions among participants. To address these issues, researchers have turned to Federated Transfer Learning (FTL), which combines transfer learning with FL. However, FTL faces unique challenges due to the continuous sharing of knowledge and lack of access to local data. This survey categorizes and reviews current progress on FTL, outlining solutions and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for people to work together on machine learning projects without sharing their personal data. But this can be tricky because each person’s data might not match up with the others’. Some researchers have tried to fix this by using something called federated transfer learning, which combines two ideas: how machines learn from new information (transfer learning) and working together on a project (federated learning). This is important because it helps people work together better. This paper looks at what’s been done so far in this area. |
Keywords
* Artificial intelligence * Federated learning * Machine learning * Transfer learning
