Summary of Towards Layer-wise Personalized Federated Learning: Adaptive Layer Disentanglement Via Conflicting Gradients, by Minh Duong Nguyen et al.
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients
by Minh Duong Nguyen, Khanh Le, Khoi Do, Nguyen H.Tran, Duc Nguyen, Chien Trinh, Zhaohui Yang
First submitted to arxiv on: 3 Oct 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 In this research paper, the authors propose a new approach to personalized Federated Learning (pFL) called Federated Learning with Layer-wise Aggregation via Gradient Analysis (FedLAG). They address the issue of high data heterogeneity causing significant gradient divergence across devices in pFL. The proposed method utilizes the concept of gradient conflict at the layer level and assigns layers for personalization based on the extent of layer-wise gradient conflicts. This approach enhances pFL performance by a certain margin, as demonstrated by theoretical evaluation and extensive experiments that show it outperforms several state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new method called Federated Learning with Layer-wise Aggregation via Gradient Analysis (FedLAG) to improve personalized Federated Learning. The authors try to solve the problem of gradient divergence in pFL when data is very different from one device to another. They do this by looking at how gradients change at each layer and grouping them together if they point in similar directions or away from each other. This helps models learn more about what’s common across devices and less about what’s specific to one device. The results show that FedLAG works better than some other methods. |
Keywords
» Artificial intelligence » Federated learning
