Cover Image

Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yijiang, Li, Zilinghan, Chard, Kyle, Foster, Ian, Munson, Todd, Madduri, Ravi, Kim, Kibaek
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
by: Li, Yijiang, et al.
Published: (2026)

Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science
by: Li, Zilinghan, et al.
Published: (2025)

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
by: Li, Zilinghan, et al.
Published: (2024)

FedSpaLLM: Federated Pruning of Large Language Models
by: Bai, Guangji, et al.
Published: (2024)

Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
by: Li, Zilinghan, et al.
Published: (2024)

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
by: Hiniduma, Kaveen, et al.
Published: (2025)

Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
by: Madduri, Ravi, et al.
Published: (2024)

FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud
by: Sinha, Aditya, et al.
Published: (2025)

Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
by: Pasini, Massimiliano Lupo, et al.
Published: (2026)

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
by: Bose, Shourya, et al.
Published: (2024)

LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
by: Jin, Hongwei, et al.
Published: (2026)

Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
by: Hudson, Nathaniel, et al.
Published: (2024)

Topology-Aware Knowledge Propagation in Decentralized Learning
by: Sakarvadia, Mansi, et al.
Published: (2025)

LUMINA: Foundation Models for Topology Transferable ACOPF
by: Li, Yijiang, et al.
Published: (2026)

A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
by: Qiu, Yuyang, et al.
Published: (2025)

Deep Model Merging: The Sister of Neural Network Interpretability -- A Survey
by: Khan, Arham, et al.
Published: (2024)

Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
by: Iakovidou, Charikleia, et al.
Published: (2024)

Towards Systematic Generalization for Power Grid Optimization Problems
by: Memon, Zeeshan, et al.
Published: (2026)

Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
by: Bose, Shourya, et al.
Published: (2024)

Towards Understanding Adversarial Transferability in Federated Learning
by: Li, Yijiang, et al.
Published: (2023)

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks
by: Guo, Zhishuai, et al.
Published: (2026)

The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators
by: Sakarvadia, Mansi, et al.
Published: (2025)

Empowering Scientific Workflows with Federated Agents
by: Kamatar, Alok, et al.
Published: (2025)

Employing Artificial Intelligence to Steer Exascale Workflows with Colmena
by: Ward, Logan, et al.
Published: (2024)

LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
by: Khan, Arham, et al.
Published: (2024)

Memory Injections: Correcting Multi-Hop Reasoning Failures during Inference in Transformer-Based Language Models
by: Sakarvadia, Mansi, et al.
Published: (2023)

UniFaaS: Programming across Distributed Cyberinfrastructure with Federated Function Serving
by: Li, Yifei, et al.
Published: (2024)

Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
by: Hudson, Nathaniel, et al.
Published: (2024)

Mitigating Memorization In Language Models
by: Sakarvadia, Mansi, et al.
Published: (2024)

QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints
by: Zeng, Sihan, et al.
Published: (2024)

GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
by: Liu, Haoyang, et al.
Published: (2025)

Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
by: Li, Xiao, et al.
Published: (2024)

Comprehensive Exploration of Synthetic Data Generation: A Survey
by: Bauer, André, et al.
Published: (2024)

Combining Language and Graph Models for Semi-structured Information Extraction on the Web
by: Hong, Zhi, et al.
Published: (2024)

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models
by: Yang, Yuning, et al.
Published: (2025)

HGNet: Scalable Foundation Model for Automated Knowledge Graph Generation from Scientific Literature
by: Joshi, Devvrat, et al.
Published: (2026)

Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
by: Lin, Jun-Liang, et al.
Published: (2026)

OmniArch: Building Foundation Model For Scientific Computing
by: Chen, Tianyu, et al.
Published: (2024)

Securing Federated Learning against Backdoor Threats with Foundation Model Integration
by: Bi, Xiaohuan, et al.
Published: (2024)