Summary of Fair Distributed Machine Learning with Imbalanced Data As a Stackelberg Evolutionary Game, by Sebastian Niehaus et al.
Fair Distributed Machine Learning with Imbalanced Data as a Stackelberg Evolutionary Game
by Sebastian Niehaus, Ingo Roeder, Nico Scherf
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computer Science and Game Theory (cs.GT); 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 This paper explores decentralized deep learning, a framework that enables the training of algorithms without centralizing data sets. This approach offers benefits such as improved data privacy, operational efficiency, and fostering data ownership policies. However, significant data imbalances arise when participants with smaller datasets perform poorer than those with larger datasets, particularly in medical fields where technological inequalities and divergent data collection practices exacerbate these imbalances. To address this challenge, the paper proposes a solution that leverages techniques from federated learning to mitigate the effects of data imbalances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us train AI models without sharing our personal data with others. Normally, training big AI models requires collecting and sharing lots of data, which can be risky for privacy. But this new approach lets many people work together on AI projects while keeping their own data private. The problem is that some people may not have as much data to share, which makes it harder for them to contribute. This research looks at ways to make sure everyone’s contributions count equally, even if they don’t have the same amount of data. |
Keywords
» Artificial intelligence » Deep learning » Federated learning
