Summary of Enabling Trustworthy Federated Learning in Industrial Iot: Bridging the Gap Between Interpretability and Robustness, by Senthil Kumar Jagatheesaperumal et al.
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness
by Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Ali Alfatemi, Nasir Ghani, Vu Khanh Quy, Abdellah Chehri
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Federated Learning (FL) revolutionizes machine learning by allowing collaborative model training while preserving data localization. This approach is particularly crucial in Industrial Internet of Things (IIoT), where data privacy, security, and efficient resource utilization are essential. FL enables learning from diverse distributed data sources without requiring central storage, enhancing privacy and reducing communication overheads. However, several challenges hinder the widespread adoption of FL in IIoT, including ensuring interpretability and robustness. This article focuses on bridging the gap between interpretability and robustness to enhance trust, improve decision-making, and ensure regulatory compliance. Design strategies are summarized to ensure transparent and reliable FL systems in IIoT, vital for industrial settings where decisions have significant safety and economic impacts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a new way of training models that lets different devices work together without sharing their data. This is important for the Industrial Internet of Things (IIoT), which needs to keep data private and secure. FL can learn from lots of data sources at once, without needing all the data in one place. But there are some challenges to making it work well, like making sure the models are easy to understand and reliable. This article talks about how to make FL more trustworthy so that people can rely on the decisions it makes. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
