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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Virtual Framework for Development and Testing of Federation Software Stack

Softwarization of networked infrastructures combined with containerization of codes promises unprecedented computing capabilities distributed across the federations of computing systems and physical instruments. The development and testing of a software stack that implements these capabilities over an expensive physical production infrastructure is not cost-effective, and in the early stages, may potentially cause service disruptions. To address these aspects, we develop the Virtual Federated Science Instrument Environment (VFSIE), a digital twin of the physical infrastructure that emulates a multi-site federation. Each federated site is emulated using containers and virtual hosts that are connected over local-area networks, and the sites, in turn, are connected over an emulated wide-area network. We describe the framework design and implementation details. We also illustrate its application by emulating a federation of four laboratories that use Jupyter Notebook for computations and the EPICS software system for instrument control.

Al Najjar, Anees↗
DOE OSTI2020

Software Framework for Federated Science Instruments

There is an unprecedented promise of enhanced capabilities for federations of leadership computing systems and experimental science facilities by leveraging software technologies for fast and efficient operations. These federations seek to unify different science instruments, both computing and experimental, to effectively support science users and operators to execute complex workflows. The FedScI project addresses the software challenges associated with the formation and operation of federated environments by leveraging recent advances in containerization of software and softwarization of hardware. We propose a software framework to streamline the federation usage by science users and it’s provisioning and operations by facility providers. A distinguishing element of our work is the support for improved interaction between experimental devices, such as beam-line instruments, and more traditional high-performance computing resources, including compute, network, storage systems. We present guiding principles for the software framework and highlight portions of a current prototype implementation. We describe our science use case involving neutron imaging beam-lines (SNAP/BL-3, Imaging/CG-1D) at the Spallation Neutron Source and High Flux Isotope Reactor facilities at Oak Ridge National Laboratory. Additionally, we detail plans for a more direct instrument interaction within a federated environment, which could enable more advanced workflows with feedback loops to shorten the time to science.

Naughton III, Thomas↗
DOE OSTI2024

Modeling Distributed Computing Infrastructures for HEP Applications

Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.

Horzela, Maximilian↗
DOE OSTI2024

Towards FAIR Workflows for Federated Experimental Sciences

A de-centralized, peer-to-peer AI metadata framework is demonstrated which can enable end-to-end metadata & lineage tracking for distributed Machine Learning pipelines spanning edge, High Performance Computing, and cloud environments. With a specific example of end-to-end microscopy algorithm and datasets, the proposed method shows how to enable reproducibility, audit trail, provenance of metadata artifacts. The emerging needs of automation in experimental sciences, ML-centric workflows, and FAIR metadata management across federated compute environments is addressed.

machine learning↗
DOE OSTI2025

Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx

Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML) projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. Specifically designed for this purpose, this work introduces APPFLx - a low-code, easy-to-use FL framework that enables easy setup, configuration, and running of FL experiments. APPFLx removes administrative boundaries of research organizations and healthcare systems while providing secure end-to-end communication, privacy-preserving functionality, and identity management. Furthermore, it is completely agnostic to the underlying computational infrastructure of participating clients, allowing an instantaneous deployment of this framework into existing computing infrastructures. Experimentally, the utility of APPFLx is demonstrated in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. Here, ML models were securely trained across heterogeneous computing resources, including a combination of on-premise high-performance computing and cloud computing facilities. By securely unlocking data from multiple sources for training without directly sharing it, these FL models enhance generalizability and performance compared to centralized training models while ensuring data remains protected. In conclusion, APPFLx demonstrated itself as an easy-to-use framework for accelerating biomedical studies across organizations and healthcare systems on large datasets while maintaining the protection of private medical data.

Biomedical Research↗
DOE OSTI2024

Converged Computing: A Best of Both Worlds of High-Performance Computing and Cloud

Collaboration between Cloud and High Performance Computing (HPC) communities has accelerated in the last half decade. A common goal to run batch workloads combined with a desire for reproducibility, automation, and optimization has led to successful projects that range from container technologies to workload management and security. This span of current and future work defines a novel “Converged Computing” paradigm that aims to combine the best of both worlds, both from a technological and cultural standpoint. Furthermore, in this Special Issue, we review common themes in the space, showcasing current work and encouraging a continued effort toward innovative ideas that will enable the next generations of scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computational Requirements in Clean Energy and Manufacturing: Summary report of the virtual workshop held on June 28-29, 2021

On June 28–29, 2021, the US Department of Energy’s (DOE’s) Advanced Scientific Computing Research (ASCR) program in the Office of Science convened a workshop with the Energy Efficiency and Renew able Energy (EERE) program offices to assess the future need for advanced computing resources in the areas of clean energy and advanced manufacturing. In part, this discussion served as an update to earlier workshops and townhalls. ASCR is guided by DOE mission needs as it develops research programs, computers, and networks at the leading edge of technologies. As the exascale computing era dawns, technology changes are creating new opportunities for those who must use high-performance computing (HPC) and data systems effectively. The ASCR computing facilities are augmenting their strategy to adapt to changing science needs and emerging technologies and to leverage the utility of exascale computing across the federal government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Federated Access from DOE Labs to Distributed Storage in the EIC Era of Computing

The Electron Ion Collider (EIC) collaboration and future experiment is a unique scientific ecosystem within Nuclear Physics as the experiment starts right off as a crosscollaboration between Brookhaven National Lab (BNL) & Jefferson Lab (JLab). As a result, this muti-lab computing model tries at best to provide services accessible from anywhere by anyone who is part of the collaboration. While the computing model for the EIC is not finalized, it is anticipated that the computational and storage resources will be made accessible to a wide range of collaborators across the world. The use of federated ID seems to be a critical element to the strategy of providing such services, allowing seamless access to each lab site computing resources. However, providing Federated access to a Federated storage is not a trivial matter and has its share of technical challenges. In this contribution, we focus on the steps we took towards the deployment of a distributed object storage system that integrates with Amazon S3 and Federated ID. We will first cover for and explain the first stage storage solutions provided to the EIC during the detector design phase. Our initial test deployment consisted of Lustre storage using MinIO, hence providing an S3 interface. High Availability load balancers were added later to provide the initial scalability it lacked. Performance of that system will be shown. While this embryonic solution worked well, it had many limitations. Looking ahead, the Ceph object storage is considered a top-of-the-line solution in the storage community - since the Ceph Object Gateway is compatible with the Amazon S3 API out of the box, our next phase will use a native S3 storage. Our Ceph deployment will consist of erasure coded storage nodes to maximize storage potential along with multiple Ceph Object Gateways for redundant access. We will compare performance of our next stage implementations. Finally, we will present how to leverage OpenID Connect with the Ceph Object Gateway’s to enable Federated ID access. We hope this contribution will serve the community needs as we move forward with cross-lab collaborations and the need for Federated ID access to distributed compute facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Federated Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tournament-Based Pretraining to Accelerate Federated Learning

Advances in hardware, proliferation of compute at the edge, and data creation at unprecedented scales have made federated learning (FL) necessary for the next leap forward in pervasive machine learning. For privacy and network reasons, large volumes of data remain stranded on endpoints located in geographically austere (or at least austere network-wise) locations. However, challenges exist to the effective use of these data. To solve the system and functional level challenges, we present an three novel variants of a serverless federated learning framework. We also present tournament-based pretraining, which we demonstrate significantly improves model performance in some experiments. Overall, these extensions to FL and our novel training method enable greater focus on science rather than ML development.

Baughman, Matt↗
DOE OSTI2026

FIRM: federated image reconstruction using multimodal tomographic data

Here, we propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality, as well as the limitations of centralized multimodal approaches that require extensive data transfer, leading to significant communication overhead, storage demands, and potential data privacy concerns. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, thereby ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence. Numerical results demonstrate superior computational efficiency and improved image reconstruction quality compared to existing approaches.

federated algorithm↗
DOE OSTI2023

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access

Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Network Anomaly Detection in Distributed Edge Computing Infrastructure

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. We show that by leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by MannWhitney U and Kolmogorov-Smirnov tests (p<0.05).

Marfo, William [University of Texas at El Paso,Dep↗
DOE OSTI2025

FEDERATED LEARNING ON STOCHASTIC NEURAL NETWORKS

Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remain on the device. However, since all data are collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as federated stochastic neural networks. In this work we will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling nonindependent and identically distributed data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Policy Considerations When Federating Facilities for Experimental and Observational Data Analysis

Today’s computational, experimental, and observational facilities afford us tremendous opportunities to couple theory and experiment at increasingly large scales. Empirical sensing capabilities are growing dramatically with beam line and detector improvements, and with advances in our ability to deploy large-scale data gathering observations of the natural world. The coupling of computational simulations and analysis to process the data from experimental and observational facilities is giving rise to cross-facility workflows. Such federations of facilities are in fact becoming an explicit requirement for large-scale scientific discovery. As we scale up these pipelines of scientific discovery, each participating facility needs to establish and align policies so that the federation can work seamlessly in an end-to-end manner. This chapter outlines specific policy considerations in enabling the federation of facilities for data analysis. Design choices and vital policy decisions cover the areas of data acquisition and storage, data transfer, computational resource allocation and co-scheduling, seamless federated user access, and cross-cutting governance. By highlighting the explicit and implicit interdependencies between facilities, we aim to provide facility designers and policymakers the information on policy issues to address early in a facility’s operations, thus enabling successful cross-facility federation and improved experimental and observational data analysis outcomes.

Shankar, Mallikarjun (Arjun)↗
DOE OSTI2021

Science Federation Emulation Testbed: Demonstration of VFSIE Functionalities

The Virtual Federated Science Instrument Environment (VFSIE) is a digital twin of a federated infrastructure of multiple sites with science instruments and computing systems that are geographically dispersed. It emulates the federation using containers and virtual hosts (vhosts) connected over local and wide-area networks for the main purpose of supporting the development and testing of science workflows and software stacks, which will subsequently transition to the physical infrastructure. In this demo, we present VFSIE of a four sites scenario and illustrate the following use cases: (i) execution of federation stack components that expose resources to enable a science user to execute workflows, (ii) execution of remote control commands of beam-line instruments to collect measurements and position the sensor, and (iii) remote execution and access of Jupyter Notebook for tomographic image computations

Al Najjar, Anees↗
DOE OSTI2026

Resource-Adaptive Federated Text Generation with Differential Privacy

In cross-silo federated learning (FL), sensitive text datasets remain confined to local organizations due to privacy regulations, making repeated training for each downstream task both communication-intensive and privacy-demanding. A promising alternative is to generate differentially private (DP) synthetic datasets that approximate the global distribution and can be reused across tasks. However, pretrained large language models (LLMs) often fail under domain shift, and federated finetuning is hindered by computational heterogeneity: only resource-rich clients can update the model, while weaker clients are excluded, amplifying data skew and the adverse effects of DP noise. We propose a flexible participation framework that adapts to client capacities. Strong clients perform DP federated finetuning, while weak clients contribute through a lightweight DP voting mechanism that refines synthetic text. To ensure the synthetic data mirrors the global dataset, we apply control codes (e.g., labels, topics, metadata) that represent each client’s data proportions and constrain voting to semantically coherent subsets. This two-phase approach requires only a single round of communication for weak clients and integrates contributions from all participants. Experiments show that our framework improves distribution alignment and downstream robustness under DP and heterogeneity.

Wang, Jiayi [ORNL]↗
DOE OSTI2024

Emerging Technologies for Privacy Preservation in Energy Systems

This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.

Cali, Umit↗