Summary of Scalable Decentralized Algorithms For Online Personalized Mean Estimation, by Franco Galante et al.
Scalable Decentralized Algorithms for Online Personalized Mean Estimation
by Franco Galante, Giovanni Neglia, Emilio Leonardi
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 a common issue in machine learning where agents lack sufficient data to learn a model. Collaborative learning can help, but it’s challenging when agents have different local data distributions. The study focuses on a simplified problem where each agent collects samples over time to estimate its mean. Existing algorithms face impractical space and time complexities. To address this, the authors propose a framework where agents self-organize into a graph, allowing for communication with only a selected number of peers. Two collaborative mean estimation algorithms are introduced: one inspired by belief propagation and the other using consensus-based approach, both with complexity O(r|A||log|A|) and O(r|A|), respectively. The authors establish conditions for asymptotically optimal estimates and provide a theoretical characterization of performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to solve a big problem in machine learning where some agents don’t have enough information to learn on their own. They look at how agents can work together to share data and improve each other’s understanding. The authors test two new algorithms that help agents communicate with just the right number of others, making it easier for them to work together effectively. |
Keywords
* Artificial intelligence * Machine learning
