Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914167626137600 |
|---|---|
| author | Lee, Youngjoon Gong, Jinu Kang, Joonhyuk |
| author_facet | Lee, Youngjoon Gong, Jinu Kang, Joonhyuk |
| contents | Federated learning enables edge devices to collaboratively train a global model while maintaining data privacy by keeping data localized. However, the Non-IID nature of data distribution across devices often hinders model convergence and reduces performance. In this paper, we propose a novel plugin for federated optimization methods that approximates Non-IID data distributions to IID through generative AI-enhanced data augmentation and balanced sampling strategy. The key idea is to synthesize additional data for underrepresented classes on each edge device, leveraging generative AI to create a more balanced dataset across the FL network. Additionally, a balanced sampling approach at the central server selectively includes only the most IID-like devices, accelerating convergence while maximizing the global model's performance. Experimental results validate that our approach significantly improves convergence speed and robustness against data imbalance, establishing a flexible, privacy-preserving FL plugin that is applicable even in data-scarce environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks Lee, Youngjoon Gong, Jinu Kang, Joonhyuk Machine Learning Signal Processing Federated learning enables edge devices to collaboratively train a global model while maintaining data privacy by keeping data localized. However, the Non-IID nature of data distribution across devices often hinders model convergence and reduces performance. In this paper, we propose a novel plugin for federated optimization methods that approximates Non-IID data distributions to IID through generative AI-enhanced data augmentation and balanced sampling strategy. The key idea is to synthesize additional data for underrepresented classes on each edge device, leveraging generative AI to create a more balanced dataset across the FL network. Additionally, a balanced sampling approach at the central server selectively includes only the most IID-like devices, accelerating convergence while maximizing the global model's performance. Experimental results validate that our approach significantly improves convergence speed and robustness against data imbalance, establishing a flexible, privacy-preserving FL plugin that is applicable even in data-scarce environments. |
| title | Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2410.23824 |