Debunking Optimization Myths in Federated Learning for Medical Image Classification
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915410964643840 |
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| author | Lee, Youngjoon Lee, Hyukjoon Gong, Jinu Cao, Yang Kang, Joonhyuk |
| author_facet | Lee, Youngjoon Lee, Hyukjoon Gong, Jinu Cao, Yang Kang, Joonhyuk |
| contents | Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy. Despite its promise in medical imaging, recent FL methods are often sensitive to local factors such as optimizers and learning rates, limiting their robustness in practical deployments. In this work, we revisit vanilla FL to clarify the impact of edge device configurations, benchmarking recent FL methods on colorectal pathology and blood cell classification task. We numerically show that the choice of local optimizer and learning rate has a greater effect on performance than the specific FL method. Moreover, we find that increasing local training epochs can either enhance or impair convergence, depending on the FL method. These findings indicate that appropriate edge-specific configuration is more crucial than algorithmic complexity for achieving effective FL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Debunking Optimization Myths in Federated Learning for Medical Image Classification Lee, Youngjoon Lee, Hyukjoon Gong, Jinu Cao, Yang Kang, Joonhyuk Machine Learning Image and Video Processing Signal Processing Federated Learning (FL) is a collaborative learning method that enables decentralized model training while preserving data privacy. Despite its promise in medical imaging, recent FL methods are often sensitive to local factors such as optimizers and learning rates, limiting their robustness in practical deployments. In this work, we revisit vanilla FL to clarify the impact of edge device configurations, benchmarking recent FL methods on colorectal pathology and blood cell classification task. We numerically show that the choice of local optimizer and learning rate has a greater effect on performance than the specific FL method. Moreover, we find that increasing local training epochs can either enhance or impair convergence, depending on the FL method. These findings indicate that appropriate edge-specific configuration is more crucial than algorithmic complexity for achieving effective FL. |
| title | Debunking Optimization Myths in Federated Learning for Medical Image Classification |
| topic | Machine Learning Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2507.19822 |