Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913213334945792 |
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| author | Wang, Jiaqi Chen, Yuzhong Wu, Yuhang Das, Mahashweta Yang, Hao Ma, Fenglong |
| author_facet | Wang, Jiaqi Chen, Yuzhong Wu, Yuhang Das, Mahashweta Yang, Hao Ma, Fenglong |
| contents | Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated learning framework. Specifically, during each communication round, we group clients into multiple clusters based on their model training status and data distribution on the server side. We then consider each cluster center as a node equipped with model parameters and construct a graph that connects these nodes using weighted edges. Additionally, we update the model parameters at each node by propagating information across the entire graph. Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side. We conduct experiments on three image benchmark datasets and create synthetic structured datasets with three types of typologies. Experimental results demonstrate the effectiveness of the proposed work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation Wang, Jiaqi Chen, Yuzhong Wu, Yuhang Das, Mahashweta Yang, Hao Ma, Fenglong Distributed, Parallel, and Cluster Computing Machine Learning Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated learning framework. Specifically, during each communication round, we group clients into multiple clusters based on their model training status and data distribution on the server side. We then consider each cluster center as a node equipped with model parameters and construct a graph that connects these nodes using weighted edges. Additionally, we update the model parameters at each node by propagating information across the entire graph. Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side. We conduct experiments on three image benchmark datasets and create synthetic structured datasets with three types of typologies. Experimental results demonstrate the effectiveness of the proposed work. |
| title | Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2401.15874 |