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Found 8 papers, 1 papers with code
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence.
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Although more layers and more parameters generally improve the accuracy of the models, such big models generally have high computational complexity and require big memory, which exceed the capacity of small devices for inference and incurs long training time.
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review
Today, to balance various trade-offs, ML-based analytics tends to increasingly leverage an interconnected ecosystem that allows complex applications to be executed on hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC systems in what is called the Computing Continuum, the Digital Continuum, or the Transcontinuum. Enabling learning-based analytics on such complex infrastructures is challenging.
Reproducible Performance Optimization of Complex Applications on the Edge-to-Cloud Continuum
We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum.
Hyperspherical embedding for novel class classification
However, this traditional approach is not useful for identifying classes unseen on the training set, known as the open set problem.
Workflow Provenance in the Lifecycle of Scientific Machine Learning
We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs.
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary.
Parallel Computation of PDFs on Big Spatial Data Using Spark
We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation.
