Search Results for author:
Found 3 papers, 2 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.
COUNTDOWN - three, two, one, low power! A Run-time Library for Energy Saving in MPI Communication Primitives
Power consumption is a looming treat in today's computing progress.
Distributed, Parallel, and Cluster Computing
