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Found 10 papers, 6 papers with code
Benchmarking FedAvg and FedCurv for Image Classification Tasks
Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres.
Experimenting with Normalization Layers in Federated Learning on non-IID scenarios
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions.
Model-Agnostic Federated Learning
MAFL marries a model-agnostic FL algorithm, AdaBoost. F, with an open industry-grade FL framework: Intel OpenFL.
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Federated Learning (FL) and Edge Inference are examples of DML.
A Federated Learning Benchmark for Drug-Target Interaction
Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs.
Modelling Biological and Ecological Systems with the Calculus of Wrapped Compartments
The modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of Ordinary Differential Equations.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data.
Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 AI diagnosis
This is the leitmotif of the convergence between HPC and AI.
Pushing the boundaries of parallel Deep Learning -- A practical approach
This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement.
