Summary of Distributed Online Optimization with Stochastic Agent Availability, by Juliette Achddou et al.
Distributed Online Optimization with Stochastic Agent Availability
by Juliette Achddou, Nicolò Cesa-Bianchi, Hao Qiu
First submitted to arxiv on: 25 Nov 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 The paper investigates a variant of distributed online optimization, where agents are active with a known probability p at each time step. A distributed FTRL algorithm is introduced and its network regret is analyzed. The analysis shows that the expected network regret after T steps is O((κ/p2)min{√N,N(1/4)/√p}√T), where κ is the condition number of the Laplacian of the communication graph G. This result is supported by experiments on synthetic datasets. The paper’s contributions include a distributed FTRL algorithm and regret bounds that hold with high probability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make learning happen when not all devices are connected at the same time. It looks at a way to update information when some devices might be missing, which is important for things like training artificial intelligence models in real-world situations. The authors create a new algorithm and show that it works well even when not all devices are talking to each other. |
Keywords
* Artificial intelligence * Optimization * Probability
