Master Federated Learning in 2 Hours—Run It on Your PC!
-
Updated
Mar 11, 2025 - Python
Master Federated Learning in 2 Hours—Run It on Your PC!
Heterogeneous Pre-trained Transformer (HPT) as Scalable Policy Learner.
You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.
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
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
KDD 2023 accepted paper, FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning
Texture Analysis test tool for PET images
A python implementation of spatial entropy
ICCV 2023 accepted paper, GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.
A general Python framework for using hidden Markov models on binary trees or cell lineage trees.
Classifying Breast Cancer Molecular Subtypes
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
Uncertainty-driven heterogeneous collective opinion dynamics, corresponding to the manuscript entitled "Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity"
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."