The repo for the FeTS Challenge

This model generate images and Discrement them on the basis of real images, gives surprising results, can apply arithmatic operations on generated images.

Official Code for PACFL AAAI 2023

FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

IEEE ICASSP 21 - Quantum Convolution Neural Networks for Speech Processing and Automatic Speech Recognition

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

In this project, I implemented the building blocks of Federated Learning (FL) and trained one from scratch on the MNIST digit data set.

Byzantine Attack-Resistant Federated Averaging Based on Outlier Elimination

Here is a basic implementation of SignSGD to assess its robustness to adversaries.

Code for our paper "Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation" (https://arxiv.org/abs/2105.10883).

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

Offical Implementation of the paper Suppressing Poisoning Attacks on Federated Learning for Medical Imaging accepted in MICCAI 2022

This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020).

Code for paper "Byzantine-Resilient Distributed Finite-Sum Optimization over Networks"

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

MXNet implementation of RAdam optimizer

R package for derivative-based variance reduction

Code for paper: Variance Reduced Local SGD with Lower Communication Complexity

A demo for VR-SGD(Comparing to some state-of-the-art algorithms).

Visualization of various deep learning optimization algorithms using PyTorch automatic differentiation and optimizers.

Neural Network optimizers implemented from scratch in numpy (Adam, Adadelta, RMSProp, SGD, etc.)

A black-box adversarial attack that exploits the local parabolic landscapes of adversarial losses

An adaptive black-box optimization method with directional Gaussian smoothing for high-dimensional multi-modal functions