Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917103404056576 |
|---|---|
| author | Ghimire, Sangam Timalsina, Paribartan Bhurtel, Nirjal Neupane, Bishal Shrestha, Bigyan Byanju Bhattarai, Subarna Gaire, Prajwal Thapa, Jessica Jha, Sudan |
| author_facet | Ghimire, Sangam Timalsina, Paribartan Bhurtel, Nirjal Neupane, Bishal Shrestha, Bigyan Byanju Bhattarai, Subarna Gaire, Prajwal Thapa, Jessica Jha, Sudan |
| contents | As the demand grows for scalable and privacy-aware AI systems, Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training without moving raw data. At the same time, the combination of high-performance computing (HPC) and cloud infrastructure offers vast computing power but introduces new complexities, especially when dealing with heterogeneous hardware, communication limits, and non-uniform data. In this work, we present a federated learning framework built to run efficiently across mixed HPC and cloud environments. Our system addresses key challenges such as system heterogeneity, communication overhead, and resource scheduling, while maintaining model accuracy and data privacy. Through experiments on a hybrid testbed, we demonstrate strong performance in terms of scalability, fault tolerance, and convergence, even under non-Independent and Identically Distributed (non-IID) data distributions and varied hardware. These results highlight the potential of federated learning as a practical approach to building scalable Artificial Intelligence (AI) systems in modern, distributed computing settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19479 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments Ghimire, Sangam Timalsina, Paribartan Bhurtel, Nirjal Neupane, Bishal Shrestha, Bigyan Byanju Bhattarai, Subarna Gaire, Prajwal Thapa, Jessica Jha, Sudan Distributed, Parallel, and Cluster Computing Machine Learning As the demand grows for scalable and privacy-aware AI systems, Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training without moving raw data. At the same time, the combination of high-performance computing (HPC) and cloud infrastructure offers vast computing power but introduces new complexities, especially when dealing with heterogeneous hardware, communication limits, and non-uniform data. In this work, we present a federated learning framework built to run efficiently across mixed HPC and cloud environments. Our system addresses key challenges such as system heterogeneity, communication overhead, and resource scheduling, while maintaining model accuracy and data privacy. Through experiments on a hybrid testbed, we demonstrate strong performance in terms of scalability, fault tolerance, and convergence, even under non-Independent and Identically Distributed (non-IID) data distributions and varied hardware. These results highlight the potential of federated learning as a practical approach to building scalable Artificial Intelligence (AI) systems in modern, distributed computing settings. |
| title | Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2511.19479 |