Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912529793417216 |
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| author | Masud, Md. Akmol Jahin, Md Abrar Hasan, Mahmud |
| author_facet | Masud, Md. Akmol Jahin, Md Abrar Hasan, Mahmud |
| contents | Federated Learning (FL) is a machine learning technique that often suffers from training instability due to the diverse nature of client data. Although utility-based client selection methods like Oort are used to converge by prioritizing high-loss clients, they frequently experience significant drops in accuracy during later stages of training. We propose a theoretical HeteRo-Select framework designed to maintain high performance and ensure long-term training stability. We provide a theoretical analysis showing that when client data is very different (high heterogeneity), choosing a smart subset of client participation can reduce communication more effectively compared to full participation. Our HeteRo-Select method uses a clear, step-by-step scoring system that considers client usefulness, fairness, update speed, and data variety. It also shows convergence guarantees under strong regularization. Our experimental results on the CIFAR-10 dataset under significant label skew ($α=0.1$) support the theoretical findings. The HeteRo-Select method performs better than existing approaches in terms of peak accuracy, final accuracy, and training stability. Specifically, HeteRo-Select achieves a peak accuracy of $74.75\%$, a final accuracy of $72.76\%$, and a minimal stability drop of $1.99\%$. In contrast, Oort records a lower peak accuracy of $73.98\%$, a final accuracy of $71.25\%$, and a larger stability drop of $2.73\%$. The theoretical foundations and empirical performance in our study make HeteRo-Select a reliable solution for real-world heterogeneous FL problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06692 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select Masud, Md. Akmol Jahin, Md Abrar Hasan, Mahmud Machine Learning Federated Learning (FL) is a machine learning technique that often suffers from training instability due to the diverse nature of client data. Although utility-based client selection methods like Oort are used to converge by prioritizing high-loss clients, they frequently experience significant drops in accuracy during later stages of training. We propose a theoretical HeteRo-Select framework designed to maintain high performance and ensure long-term training stability. We provide a theoretical analysis showing that when client data is very different (high heterogeneity), choosing a smart subset of client participation can reduce communication more effectively compared to full participation. Our HeteRo-Select method uses a clear, step-by-step scoring system that considers client usefulness, fairness, update speed, and data variety. It also shows convergence guarantees under strong regularization. Our experimental results on the CIFAR-10 dataset under significant label skew ($α=0.1$) support the theoretical findings. The HeteRo-Select method performs better than existing approaches in terms of peak accuracy, final accuracy, and training stability. Specifically, HeteRo-Select achieves a peak accuracy of $74.75\%$, a final accuracy of $72.76\%$, and a minimal stability drop of $1.99\%$. In contrast, Oort records a lower peak accuracy of $73.98\%$, a final accuracy of $71.25\%$, and a larger stability drop of $2.73\%$. The theoretical foundations and empirical performance in our study make HeteRo-Select a reliable solution for real-world heterogeneous FL problems. |
| title | Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.06692 |