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airpermeabilityprediction

anp_backdoor

Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

awesome-fl

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

azureml-tutorial

Azure Machine Learning Tutorial (CLI / Python)

bach2018

PYCON 2018 and BACH 2018 breast cancer histology challenge

best-practices

blades

Blades: A simulator and benchmark for Byzantine-robust federated Learning with Attacks and Defenses Experimental Simulation

brain-tumor-classification-using-transfer-learning

Machine Learning Course Project

cip

confedmade

cooperative-disjuctive-brb

covid-19-clinical

covid-ctset

Large Covid-19 CT scans dataset from the paper: https://doi.org/10.1016/j.bspc.2021.102588

crystal

Defending Privacy Against More Knowledgeable Membership Inference Attackers

datasettag

KeyPhrase labeling of scientiific articales using word embedding and bidirectional lstm network

deep-learning-and-transfer-learning-features-for-plankton-classification

deepphopred

dna2vec

dna2vec: Consistent vector representations of variable-length k-mers

dna_binding

evofuzzy

A Python implementation of the Differential Evolution algorithm for the optimization of Fuzzy Inference Systems.

explainable_neural_network

Develop a neural network with shap value explanation. Demo on tubular data.

faisal

federated

A collection of Google research projects related to Federated Learning and Federated Analytics.

federated-learning-pytorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

federatedlearning

Material for the course "Federated Learning"

fedpso

FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

fedsr

frauddetection

Accounting Fraud Detection Using Machine Learning

geneticalgorithm-solarcells

Single and Multi-layer Solar Cell Thickness Optimization With Genetic Algorithm [Energies 2020]

gradattack

GradAttack is a Python library for easy evaluation of privacy risks in public gradients in Federated Learning, as well as corresponding mitigation strategies.