37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.

📬 A knowledge graph querying framework for JavaScript

Heterogeneous Pre-trained Transformer (HPT) as Scalable Policy Learner.

PureEdgeSim: A simulation framework for performance evaluation of cloud, fog, and pure edge computing environments.

You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.

HMM-integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data

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…

This is a platform containing the datasets and federated learning algorithms in IoT environments.

CVPR 2024 accepted paper, An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

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goal of this project is to improve and update aspects of existing benchmarking suites which are either insufficient or outdated.

AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning

Heterogeneous Multi-Robot Reinforcement Learning

Spatial analysis toolkit for single cell multiplexed tissue data

QGIS plugin of geographical detector

Texture Analysis test tool for PET images

KDD 2023 accepted paper, FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

A python implementation of spatial entropy

WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.

Inspired by Hillebrand & Medeiros (2009) and Corsi (2009), I put neural networks in a High frequency environment, and tested the performance of the two models (HAR & Neural Networks). - The data used in this project is 2 years worth of intraday 5-minute realized volatility (See: Sheppard, Patton, Liu, 2012) from 20 Dow Jones stocks, that has bee…