Federated Learning Framework for Scalable AI in Heterogeneous HPC and Cloud Environments
This addresses the need for scalable and privacy-aware AI systems in distributed computing settings, but it appears incremental as it builds on existing federated learning methods.
The paper tackled the problem of efficiently running federated learning across heterogeneous HPC and cloud environments, demonstrating strong performance in scalability, fault tolerance, and convergence under non-IID data and varied hardware.
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.