Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments

• URL: http://arxiv.org/abs/2511.19479v1
• Date: Sat, 22 Nov 2025 18:39:25 GMT
• Title: Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments
• Authors: Sangam Ghimire, Paribartan Timalsina, Nirjal Bhurtel, Bishal Neupane, Bigyan Byanju Shrestha, Subarna Bhattarai, Prajwal Gaire, Jessica Thapa, Sudan Jha,
• Abstract summary: We present a federated learning framework built to run efficiently across mixed HPC and cloud environments.<n>Our system addresses key challenges such as system het- erogeneity, communication overhead, and resource scheduling, while maintaining model accuracy and data privacy.
• Score: 0.1805840413757548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 heteroge- neous 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 het- erogeneity, 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.

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