Summary of Addressing Heterogeneity in Federated Learning: Challenges and Solutions For a Shared Production Environment, by Tatjana Legler et al.
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment
by Tatjana Legler, Vinit Hegiste, Ahmed Anwar, Martin Ruskowski
First submitted to arxiv on: 18 Aug 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 The paper explores the challenges and opportunities of federated learning (FL) in manufacturing and shared production environments, where data heterogeneity poses significant issues. FL trains machine learning models across decentralized data sources while preserving privacy. The study highlights various types of heterogeneity, including non-IID data, unbalanced data, variable data quality, and statistical heterogeneity, which impact model training. Current methodologies for mitigating these effects include personalized models, robust aggregation techniques, and client selection techniques. The paper aims to provide insights for managing data heterogeneity in FL, enhancing model robustness, and ensuring fair and efficient training across diverse environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers look at how to make machine learning work better when different factories or sites share their data. They find that differences in the way data is collected can cause problems. To solve these issues, they review ways to adapt models to each site’s data, use special techniques to combine data, and choose which sites to include in training. The goal is to make machine learning fair and efficient across different environments. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
