PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916800481984512 |
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| author | Li, Liangyan Liu, Yangyi Ning, Yimo Rini, Stefano Chen, Jun |
| author_facet | Li, Liangyan Liu, Yangyi Ning, Yimo Rini, Stefano Chen, Jun |
| contents | Federated Learning (FL) has emerged as a powerful paradigm for leveraging diverse datasets from multiple sources while preserving data privacy by avoiding centralized storage. However, many existing approaches fail to account for the intricate gradient correlations between remote clients, a limitation that becomes especially problematic in data heterogeneity scenarios. In this work, we propose a novel FL framework utilizing Power-Norm Cosine Similarity (PNCS) to improve client selection for model aggregation. By capturing higher-order gradient moments, PNCS addresses non-IID data challenges, enhancing convergence speed and accuracy. Additionally, we introduce a simple algorithm ensuring diverse client selection through a selection history queue. Experiments with a VGG16 model across varied data partitions demonstrate consistent improvements over state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning Li, Liangyan Liu, Yangyi Ning, Yimo Rini, Stefano Chen, Jun Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing Federated Learning (FL) has emerged as a powerful paradigm for leveraging diverse datasets from multiple sources while preserving data privacy by avoiding centralized storage. However, many existing approaches fail to account for the intricate gradient correlations between remote clients, a limitation that becomes especially problematic in data heterogeneity scenarios. In this work, we propose a novel FL framework utilizing Power-Norm Cosine Similarity (PNCS) to improve client selection for model aggregation. By capturing higher-order gradient moments, PNCS addresses non-IID data challenges, enhancing convergence speed and accuracy. Additionally, we introduce a simple algorithm ensuring diverse client selection through a selection history queue. Experiments with a VGG16 model across varied data partitions demonstrate consistent improvements over state-of-the-art methods. |
| title | PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning |
| topic | Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.15923 |