Summary of Towards Optimal Customized Architecture For Heterogeneous Federated Learning with Contrastive Cloud-edge Model Decoupling, by Xingyan Chen and Tian Du and Mu Wang and Tiancheng Gu and Yu Zhao and Gang Kou and Changqiao Xu and Dapeng Oliver Wu
Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling
by Xingyan Chen, Tian Du, Mu Wang, Tiancheng Gu, Yu Zhao, Gang Kou, Changqiao Xu, Dapeng Oliver Wu
First submitted to arxiv on: 4 Mar 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 A novel federated learning framework, called FedCMD, is proposed to tackle the challenges of distributed training across multiple edge clients. FedCMD decouples deep neural networks into a shared body and personalized head, allowing for better representation of local data heterogeneity. The motivation behind this approach is that assigning the last layer as the personalized head may not always be optimal. Instead, a dynamic selection method is introduced to identify the best-match layer for personalization based on representation difference between neighbor layers. A weighted global aggregation algorithm is also proposed to accommodate the selected personalized layer. Experimental results on ten benchmarks demonstrate the efficiency and superior performance of FedCMD compared with nine state-of-the-art solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedCMD is a new way for computers to learn together without sharing all their data. This helps when devices have different types of information, which can slow down learning. To solve this problem, FedCMD separates neural networks into two parts: one that learns shared ideas and another that adapts to each device’s unique data. The key innovation is selecting the best part of the network to adapt to each device. By doing so, FedCMD achieves better results than previous methods on ten different datasets. |
Keywords
* Artificial intelligence * Federated learning
