Summary of Optimizing Personalized Federated Learning Through Adaptive Layer-wise Learning, by Weihang Chen et al.
Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning
by Weihang Chen, Jie Ren, Zhiqiang Li, Ling Gao, Zheng Wang
First submitted to arxiv on: 10 Dec 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 The proposed FLAYER method is a novel layer-wise learning approach for personalized federated learning (pFL) that optimizes local model personalization performance while preserving global knowledge. By considering the different roles and learning abilities of neural network layers in individual local models, FLAYER initializes local models cost-effectively by incorporating global information as needed. The method dynamically adjusts learning rates for each layer during local training to optimize the personalized learning process. Additionally, FLAYER selectively uploads parameters for global aggregation in a layer-wise manner to enhance global representation in pFL. Experimental results on four representative datasets show that FLAYER improves inference accuracy, on average, by 7.21% compared to six state-of-the-art pFL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning (FL) is a way to train machine learning models without sharing data. But sometimes, this approach doesn’t work well because the data is different from what was used to train the model. Personalized FL (pFL) tries to fix this by tailoring the local models to each individual’s data. However, current pFL methods have some problems, like not being able to share global information efficiently and over-personalizing their data. The proposed FLAYER method solves these issues by considering the different roles of neural network layers in individual local models. It incorporates global information when needed and dynamically adjusts learning rates during local training. This results in better performance and more accurate predictions. |
Keywords
» Artificial intelligence » Federated learning » Inference » Machine learning » Neural network
