Summary of On the Necessity Of Collaboration For Online Model Selection with Decentralized Data, by Junfan Li and Zheshun Wu and Zenglin Xu and Irwin King
On the Necessity of Collaboration for Online Model Selection with Decentralized Data
by Junfan Li, Zheshun Wu, Zenglin Xu, Irwin King
First submitted to arxiv on: 15 Apr 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 explores the necessity of collaboration among clients in decentralized data settings with M clients. From a novel perspective of computational constraints, the authors prove lower bounds on regret and propose a federated algorithm to analyze its upper bound results show that (i) collaboration is unnecessary without computational constraints; (ii) it’s necessary if the cost on each client is limited to o(K), where K is the number of candidate hypothesis spaces. The paper improves regret bounds at a smaller computational and communication cost, relying on three new techniques: improved Bernstein’s inequality for martingale, federated online mirror descent framework, and decoupling model selection and prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists study how machines learn from lots of different data sent by many devices. They want to know if these devices need to work together or not. The authors found that without limits on the computers’ power, they don’t really need to collaborate. But if there are limits, they do need to work together. This helps us make better learning machines that use less energy and data. |
