Summary of From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare, by Ming Li et al.
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
by Ming Li, Pengcheng Xu, Junjie Hu, Zeyu Tang, Guang Yang
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The abstract discusses the potential of Federated Learning (FL) for large-scale healthcare research while preserving data privacy and security. It reviews recent studies using FL-based methods in healthcare, concluding that most are not clinically applicable due to methodological flaws and biases. The study highlights challenges like privacy concerns, generalization issues, and communication costs, which compromise the effectiveness of FL in healthcare. Recommendations are provided to overcome these hurdles and improve model development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for computers to learn together without sharing their data. It could help scientists work together on big health projects while keeping patient information private. A new study looked at recent research using this method in healthcare. Unfortunately, most of the studies had serious problems that make them not suitable for real-world use. The biggest issues were related to privacy, making sure results are accurate, and communication between computers. To fix these problems, scientists need to work together to create better models. |
Keywords
» Artificial intelligence » Federated learning » Generalization
