Summary of Geographical Node Clustering and Grouping to Guarantee Data Iidness in Federated Learning, by Minkwon Lee et al.
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning
by Minkwon Lee, Hyoil Kim, Changhee Joo
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 proposes a novel approach to ensure data independence and identicalness (IID) for federated learning (FL) in smart IoT networks. FL is a decentralized AI mechanism that trains models on edge devices, but the non-IID dataset problem arises when heterogeneous data from different devices is collected. The authors provide experimental evidence that IoT device distance can be used to achieve IID features in datasets. They propose Dynamic Clustering and Partial-Steady Grouping algorithms to partition FL participants into groups that achieve near-IIDness in their datasets while considering device mobility. The proposed mechanism outperforms benchmark grouping algorithms by at least 110 times in terms of joint cost, with a mild increase in the number of groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to help devices in smart IoT networks work together better when sharing data for training AI models. This is important because the devices collect different types of data, which can make it hard for the models to learn from all the data at once. The authors show that by grouping devices based on their distance from each other, they can create groups with similar data that will help the AI models work better. They also compare their method to others and show that it is much more effective. |
Keywords
» Artificial intelligence » Clustering » Federated learning
