Summary of Empowering Data Mesh with Federated Learning, by Haoyuan Li and Salman Toor
Empowering Data Mesh with Federated Learning
by Haoyuan Li, Salman Toor
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel approach to distributed machine learning is proposed in this paper, which tackles the limitations of centralized data architectures like data lakes. The authors introduce Data Mesh, a decentralized framework that assigns ownership of data domains to local teams while maintaining federated governance. This shift enables timely analysis and processing across multiple domains. However, traditional machine learning methods are insufficient for analyzing data across these domains, particularly in security-sensitive organizations. To address this, the paper presents an innovative approach that combines Federated Learning with Data Mesh, marking a significant step towards privacy-preserving and decentralized data analysis strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have lots of different data sources, like pictures from your phone, emails, and social media posts. Storing all this data in one place can be tricky, so scientists came up with a new way to organize it called Data Mesh. Instead of putting everything in one bucket, they broke the data into smaller groups based on what’s related (like all your phone photos). Each group has its own team that takes care of the data. This makes it easier to analyze and use the data quickly, but traditional machine learning methods struggle to work with this decentralized setup. The authors came up with a new way to combine Data Mesh with another technology called Federated Learning, which helps keep your data private and secure. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
