Summary of Cobo: Collaborative Learning Via Bilevel Optimization, by Diba Hashemi et al.
CoBo: Collaborative Learning via Bilevel Optimization
by Diba Hashemi, Lie He, Martin Jaggi
First submitted to arxiv on: 9 Sep 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 A novel bilevel optimization approach is proposed for collaborative learning, which models client-selection and model-training as interconnected problems. The CoBo algorithm, an SGD-type alternating optimization method, efficiently addresses these challenges with theoretical convergence guarantees. Empirically, CoBo achieves superior performance, outperforming popular personalization algorithms by 9.3% in accuracy on a task with high heterogeneity involving datasets distributed among 80 clients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Collaborative learning helps multiple people learn together better. But finding the right people to help can be hard and takes up a lot of time. In this paper, researchers created a new way to solve two connected problems: choosing who to ask for help and training a model with that help. They call their solution CoBo, an algorithm that works well and is efficient. Tests show that CoBo does better than other methods by 9.3% in accuracy when working with lots of different people and datasets. |
Keywords
» Artificial intelligence » Optimization
