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

TL;DR

This paper introduces a federated learning framework designed for efficient, scalable AI deployment across heterogeneous HPC and cloud environments, addressing challenges like system diversity, communication costs, and data privacy.

Contribution

It presents a novel federated learning system optimized for mixed HPC and cloud infrastructures, improving scalability, fault tolerance, and convergence under diverse hardware and data conditions.

Findings

Demonstrates high scalability and fault tolerance in hybrid environments.

Achieves effective model convergence with non-IID data distributions.

Maintains data privacy while optimizing communication overhead.

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 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.