Summary of From Learning to Analytics: Improving Model Efficacy with Goal-directed Client Selection, by Jingwen Tong et al.
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
by Jingwen Tong, Zhenzhen Chen, Liqun Fu, Jun Zhang, Zhu Han
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 closed-loop model analytics framework is proposed to evaluate the well-trained global model in federated learning (FL) while addressing system and data heterogeneities through goal-directed client selection. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem and solved using Quick-Init UCB and BP-UCB algorithms under federated analytics (FA) and democratized analytics (DA) frameworks, respectively. The proposed methods achieve nearly optimal performance with regret upper bounds increasing logarithmically over the time horizon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for different devices to learn from each other without sharing their personal data. To make sure this process works well, scientists need to test how good the final model is. They propose a new system that can do this testing and choose which devices to use in the learning process based on how well they fit together. This helps solve some big problems with making sure all the devices are working together correctly. The results show that their method works really well, almost as good as if they had access to all the data. |
Keywords
» Artificial intelligence » Federated learning
