Summary of Fmlfs: a Federated Multi-label Feature Selection Based on Information Theory in Iot Environment, by Afsaneh Mahanipour et al.
FMLFS: A Federated Multi-Label Feature Selection Based on Information Theory in IoT Environment
by Afsaneh Mahanipour, Hana Khamfroush
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Networking and Internet Architecture (cs.NI)
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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 This paper introduces FMLFS, a federated multi-label feature selection method designed for distributed IoT environments. The method addresses challenges posed by noisy, redundant, or irrelevant features in massive multi-label datasets generated by IoT devices. FMLFS employs mutual information between features and labels as the relevancy metric and correlation distance as the redundancy measure. The approach is evaluated through two scenarios: transmitting reduced-size datasets to an edge server for centralized classifier usage and employing federated learning with reduced-size datasets. The proposed method outperforms five comparable methods in the literature across three metrics – performance, time complexity, and communication cost – on three real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make it easier for devices connected to the internet (IoT) to work together and share information. These devices create a lot of data that can be hard to use because some parts are useless or repeated. The authors created a new way, called FMLFS, to help sort through this data and pick out what’s most important. They tested their method on different datasets and found it worked better than other methods they tried. This could be useful for things like monitoring traffic or health, where devices need to share information to make decisions. |
Keywords
» Artificial intelligence » Feature selection » Federated learning
