Summary of Seamless Integration: Sampling Strategies in Federated Learning Systems, by Tatjana Legler et al.
Seamless Integration: Sampling Strategies in Federated Learning Systems
by Tatjana Legler, Vinit Hegiste, Martin Ruskowski
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 tackles the complexities of integrating new clients into existing Federated Learning (FL) systems. In a decentralized training setting, FL offers a privacy-preserving approach to model training across multiple devices. However, as new clients join the network with diverse data distributions and computational capabilities, it poses challenges to system stability and efficiency. The paper explores how data heterogeneity affects model training, system efficiency, scalability, and stability. Despite these challenges, integrating new clients can enhance data diversity, improve learning performance, and leverage distributed computational power. Strategies for effective client selection and solutions for ensuring system scalability are proposed, using the example of optical quality inspection images. The findings have implications for the adoption of FL in production environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making sure that when new devices join a network to learn together without sharing their data, everything runs smoothly and efficiently. It’s like adding new people to a team working on a project, but instead of just doing tasks, they’re learning from each other. The researchers found that having different types of data and computing power can be both good and bad for the overall performance. They came up with ways to choose which devices join the network and how to make sure it all works together well. |
Keywords
» Artificial intelligence » Federated learning
