Summary of Federated Model Heterogeneous Matryoshka Representation Learning, by Liping Yi et al.
Federated Model Heterogeneous Matryoshka Representation Learning
by Liping Yi, Han Yu, Chao Ren, Gang Wang, Xiaoguang Liu, Xiaoxiao Li
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Federated model heterogeneous Matryoshka Representation Learning (FedMRL) approach enables clients to collaboratively train models with heterogeneous structures in a distributed fashion. By adding an auxiliary small homogeneous model shared by clients, FedMRL facilitates knowledge exchange between client and server models, achieving better representation fusion and multi-perspective learning. Theoretical analysis demonstrates non-convex convergence rate of O(1/T), while extensive experiments on benchmark datasets show improved model accuracy with low communication and computational costs compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model heterogeneous federated learning (MHeteroFL) is a way for different devices to work together and train models. The problem is that current methods don’t share knowledge well between the devices. To fix this, we created FedMRL, which adds a small model that all devices use. This helps devices learn from each other better. We showed that FedMRL works well on lots of data sets and can improve accuracy. |
Keywords
» Artificial intelligence » Federated learning » Representation learning
