MFCL-NeurIPS23
Data Synthesis Framework
A framework for mitigating catastrophic forgetting in federated learning for vision tasks using data synthesis from past distributions.
15 stars
2 watching
3 forks
Language: Python
last commit: 7 months ago Related projects:
Repository | Description | Stars |
---|---|---|
drckf/paysage | An unsupervised learning and generative models library for Python, focusing on probabilistic models and efficient computation. | 119 |
sarapieri/fed_het | This project investigates how to design architectures that enable better performance in federated learning systems, particularly for visual recognition tasks. | 10 |
umbc-sanjaylab/fedpseudo_kdd23 | This repository provides an implementation of federated survival analysis using a deep learning framework. | 0 |
mediabrain-sjtu/fedgela | Federated learning algorithm designed to handle partially class-disjoint data by utilizing bilateral curation and Dirichlet partitioning. | 10 |
dawenzi098/sfl-structural-federated-learning | A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 50 |
nvlabs/edm | This project provides a set of tools and techniques to design and improve diffusion-based generative models. | 1,399 |
andytu28/fps_pre-training | Implementation of a pre-training technique for improving the performance of neural networks on image data | 4 |
mediabrain-sjtu/pfedgraph | This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |
jhoon-oh/fedbabu | An implementation of federated learning for image classification tasks | 51 |
woozzu/dong_iccv_2017 | An implementation of semantic image synthesis via adversarial learning using PyTorch | 145 |
omarfoq/knn-per | A federated learning framework with personalized memorization using deep neural networks and k-nearest neighbors for collaborative learning of statistical models | 42 |
omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 154 |
conditionwang/fcil | Implementation of Federated Class-Incremental Learning for Continual Learning in Computer Vision | 101 |
chuanli11/cnnmrf | An algorithm combining Markov Random Fields and Convolutional Neural Networks for generating synthetic images based on input content and style. | 865 |
krishnap25/fl_partial_personalization | A framework for federated learning with partial model personalization | 2 |