Summary of Federated Learning in Chemical Engineering: a Tutorial on a Framework For Privacy-preserving Collaboration Across Distributed Data Sources, by Siddhant Dutta et al.
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources
by Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier, Claudio Miceli de Farias, David Esteban Bernal Neira
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Neural and Evolutionary Computing (cs.NE)
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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 new paper introduces Federated Learning (FL) to the chemical engineering community, providing a comprehensive guide and hands-on tutorial for applying FL to tasks like manufacturing optimization, multimodal data integration, and drug discovery. The tutorial uses frameworks like Flower and TensorFlow Federated, allowing chemical engineers to adopt FL in their specific needs. The study compares FL’s performance against centralized learning on three datasets relevant to chemical engineering applications, showing that FL maintains or improves classification performance, especially for complex and heterogeneous data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for different computers to work together on a project without sharing sensitive information. This can be helpful in the chemical industry where companies may have valuable secrets. A new paper explains how to use Federated Learning to solve problems like optimizing manufacturing processes, combining different types of data, and discovering new medicines. The tutorial helps readers understand how to apply this technology by using special software frameworks. The study tested Federated Learning on three sets of data related to the chemical industry and found that it often performs as well or better than traditional learning methods. |
Keywords
* Artificial intelligence * Classification * Federated learning * Optimization
