Summary of Interplay Between Federated Learning and Explainable Artificial Intelligence: a Scoping Review, by Luis M. Lopez-ramos et al.
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
by Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks, Inga Strümke, Vince I. Madai, Tobias Budig, Ali Sunyaev, Adam Hilbert
First submitted to arxiv on: 7 Nov 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 joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) enables the training of models from distributed data while preserving privacy. This scoping review identifies publications that jointly explore FL and XAI, focusing on those where an interplay between FL and model interpretability or post-hoc explanations was found. The study highlights the benefits and tensions associated with combining these two technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines Federated learning (FL) and Explainable artificial intelligence (XAI). It trains models using data from many sources while keeping private information safe. The researchers looked at papers that use both FL and XAI together, focusing on those where the combination of these techniques helps explain how models work or makes them more understandable after they’re trained. |
Keywords
* Artificial intelligence * Federated learning
