Summary of Personalized Federated Learning with Mixture Of Models For Adaptive Prediction and Model Fine-tuning, by Pouya M. Ghari and Yanning Shen
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning
by Pouya M. Ghari, Yanning Shen
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 proposed algorithm develops a novel personalized federated learning method that combines local fine-tuning with federated models, enhancing performance in dynamic environments. This approach addresses the challenge of using pre-trained models in non-stationary settings by allowing clients to fine-tune their models online. Theoretical analysis and experiments on real datasets demonstrate the effectiveness of this algorithm for real-time predictions and federated model fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn from data is being developed, which lets devices work together without sharing their own information. This is useful when you need to make quick decisions based on changing data. The current method isn’t very good at doing this because it’s slow and doesn’t adapt well to changes. To fix this, a personalized approach was created that combines the device’s own learning with what other devices have learned together. This helps make better predictions in real-time and improves the way devices work together. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning
