We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study
-
Updated
Jun 7, 2024 - Python
We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study
📬 A knowledge graph querying framework for JavaScript
PureEdgeSim: A simulation framework for performance evaluation of cloud, fog, and pure edge computing environments.
HMM-integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data
an R package for testing, estimating and evaluating the Panel Smooth Transition Regression (PSTR) model.
A benchmarking suite for heterogeneous systems. The primary goal of this project is to improve and update aspects of existing benchmarking suites which are either insufficient or outdated.
PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous. Several mobile-friendly models are prov…
Heterogeneous Multi-Robot Reinforcement Learning
Texture Analysis test tool for PET images
Classifying Breast Cancer Molecular Subtypes
This is a platform containing the datasets and federated learning algorithms in IoT environments.
A python implementation of spatial entropy
QGIS plugin of geographical detector
Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning
MicroRaman is an R package to process microbial Raman Spectroscopy data
Source data and code supporting model-based analysis of emergent properties (ECA) of soil enzyme-driven organic matter decomposition
The development version of the frailtyEM R package, that fits several types of shared frailty models
Compartmentalized models with Argiope
Rossman R., Yackulic C., Saunders S.P., Reid J., Davis R., and Zipkin E.F. 2016. Dynamic N-occupancy models: estimating demographic rates and local abundances from detection-nondetection data. Ecology. 97: 3300-3307.
Add a description, image, and links to the heterogeneity topic page so that developers can more easily learn about it.
To associate your repository with the heterogeneity topic, visit your repo's landing page and select "manage topics."