Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912118040690688 |
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| author | Sun, Pengcheng Liu, Erwu Ni, Wei Yu, Kanglei Qu, Xinyu Wang, Rui Bi, Yanlong Zhang, Chuanchun Jamalipour, Abbas |
| author_facet | Sun, Pengcheng Liu, Erwu Ni, Wei Yu, Kanglei Qu, Xinyu Wang, Rui Bi, Yanlong Zhang, Chuanchun Jamalipour, Abbas |
| contents | Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. This is achieved by defining a new signal-to-noise ratio (SNR) matrix and information quantity matrix to capture the communication and data heterogeneity, respectively. The celebrated affinity propagation algorithm is leveraged to iteratively refine the clustering of clients based on the newly defined matrices effectively enhancing model aggregation in heterogeneous environments. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL and demonstrate superior accuracy in heterogeneous environments compared to classical clustering methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments Sun, Pengcheng Liu, Erwu Ni, Wei Yu, Kanglei Qu, Xinyu Wang, Rui Bi, Yanlong Zhang, Chuanchun Jamalipour, Abbas Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. This is achieved by defining a new signal-to-noise ratio (SNR) matrix and information quantity matrix to capture the communication and data heterogeneity, respectively. The celebrated affinity propagation algorithm is leveraged to iteratively refine the clustering of clients based on the newly defined matrices effectively enhancing model aggregation in heterogeneous environments. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL and demonstrate superior accuracy in heterogeneous environments compared to classical clustering methods. |
| title | Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.09276 |