Summary of Fedselect: Personalized Federated Learning with Customized Selection Of Parameters For Fine-tuning, by Rishub Tamirisa et al.
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
by Rishub Tamirisa, Chulin Xie, Wenxuan Bao, Andy Zhou, Ron Arel, Aviv Shamsian
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the issue of client data heterogeneity in standard federated learning approaches by proposing a novel personalized federated learning algorithm called FedSelect. The existing methods for personalized federated learning typically decouple global updates in deep neural networks, but this may result in suboptimal storage of global knowledge. Instead, FedSelect incrementally expands subnetworks to personalize client parameters while concurrently conducting global aggregations on the remaining parameters. This approach enables personalization of both client parameters and subnetwork structure during training. The authors demonstrate that FedSelect outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and shows robustness to various real-world distributional shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a type of artificial intelligence called federated learning work better when different devices have very different types of information. Right now, this kind of learning can struggle if the devices have too much difference in what they know. To fix this problem, the authors created a new way to personalize the learning process for each device while still sharing information with other devices. This helps the devices learn from each other and improve their own knowledge better. The new method is called FedSelect and it works by gradually building up smaller groups of devices that can work together to share information and learn from each other. |
Keywords
* Artificial intelligence * Federated learning
