Summary of Collaborative and Federated Black-box Optimization: a Bayesian Optimization Perspective, by Raed Al Kontar
Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective
by Raed Al Kontar
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A medium-difficulty summary: This paper focuses on collaborative black-box optimization (BBOpt), where multiple agents optimize their own functions through shared experimentation. The authors address challenges like distributed experimentation, heterogeneity, and privacy within BBOpt from a Bayesian optimization perspective. They propose three frameworks to tackle these issues: global coordination, local decision-making with minimal sharing, and predictive surrogates enhanced through collaboration. The paper categorizes existing methods within these frameworks and highlights open questions to unlock the full potential of federated BBOpt. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A low-difficulty summary: This research is about how multiple agents can work together to find the best solution for their own problems. These agents are trying to optimize different functions, but they need to share some information with each other to make good decisions. The authors come up with three ways that these agents can work together: by sharing all their information, by only sharing a little bit of information, and by using models to predict what will happen if they do certain things. The goal is to make it easier for agents to work together to find the best solution. |
Keywords
* Artificial intelligence * Optimization
