Machine LearningProtecting Privacy in Decentralized Learning ModelsExploring methods to enhance privacy in decentralized learning while maintaining performance.
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A new program improves fluid flow calculations using Regent and LSKUM methods.
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A method using Sentinel-2 images aids in labeling polar sea ice automatically.
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A new framework aims to cut carbon footprints in large language models.
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A new approach combining weight and token pruning for efficient image processing.
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Streamlining the calibration process for more accurate simulations in distributed computing.
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Dynamic adjustment of data structures for efficient performance under varying workloads.
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Discover how distributed algorithms streamline resource allocation among agents.
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FedMef improves federated learning for low-resource devices through innovative pruning techniques.
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Exploring methods to achieve faster transaction finality on the Ethereum network.
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A novel method using Fourier transforms for better data moment estimation.
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New methods enhance the Kaczmarz algorithm for solving dense linear systems.
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A new framework for training models without sharing sensitive data.
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Combining RADICAL-Pilot and Cylon for efficient data management in deep learning.
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Union simplifies managing hybrid workloads in high-performance computing environments.
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This method improves routing decisions in Dragonfly networks using machine learning.
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MRSch improves high-performance computing job scheduling by optimizing resource use.
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A new approach uses decision trees for clearer job scheduling in high-performance computing.
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Intelligent routing methods improve performance in Dragonfly supercomputing networks.
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A look at how DRM improves resource allocation in computing.
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A new protocol improves the training efficiency of Graph Neural Networks using CPU and GPU.
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Explore the shift from CPUs to GPUs in sea ice modeling.
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Examining smart contracts, their security, and the role of trust management.
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A new method enhances privacy in online federated learning while adapting to changing data.
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Raptor improves serverless function scheduling for faster, more reliable applications.
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Data Mesh transforms data management by fostering team ownership and collaboration.
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Learn about methods for identifying even cycles in large networks and their importance.
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Strategies to reduce cold start latency in serverless applications.
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An overview of the Raft algorithm for consensus in distributed systems.
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Exploring the intersection of blockchain and federated learning for data privacy and security.
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THEMIS improves fairness and efficiency in multi-tenant FPGA resource scheduling.
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FedD2S enhances Federated Learning by personalizing models while preserving data privacy.
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A framework for training machine learning models while protecting privacy.
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A new framework aims to improve DSL interoperability and efficiency in HPC.
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A system that optimizes calculations for sparse matrices using blocked storage.
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This paper discusses DAOS's potential to enhance weather prediction data management.
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Research focuses on optimizing transformers for small devices with limited resources.
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Learn how history trees improve agent interactions in dynamic networks.
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MOPAR improves efficiency and cost-effectiveness of deep learning inference on serverless platforms.
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Synthetic data enables companies to share insights while protecting sensitive information.
― 5 min read