pFedBayes

Personalized Fed Learning Model

An implementation of personalized federated learning using variational Bayesian inference on the MNIST dataset

Personalized Federated Learning via Variational Bayesian Inference [ICML 2022]

GitHub

52 stars
1 watching
7 forks
Language: Python
last commit: about 4 years ago
bayesianbayesian-neural-networkfederated-learningvariational-bayesian-inference

Related projects:

RepositoryDescriptionStars
mediabrain-sjtu/pfedgraphThis 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
ignavierng/notears-admmAn implementation of Bayesian network structure learning with continuous optimization for federated learning.10
idanachituve/pfedgpAn implementation of Personalized Federated Learning with Gaussian Processes using Python.32
royson/fedl2pThis project enables personalized learning models by collaborating on learning the best strategy for each client19
jinheonbaek/fed-pubPersonalized Subgraph Federated Learning framework for distributed machine learning45
bdemo/pfedbred_publicA project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models9
mehdiset/perfedmaskA PyTorch implementation of personalized federated learning with optimized masking vectors for various machine learning tasks and datasets.15
charliedinh/pfedmeAn implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation.291
dawenzi098/sfl-structural-federated-learningA Python implementation of Personalized Federated Learning with Graph using PyTorch.49
pengyang7881187/fedrlEnabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data56
xiyuanyang45/dynamicpflA method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness57
hui-po-wang/progfedAn approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements.20
aiot-mlsys-lab/fedrolexAn approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources.61
krishnap25/fl_partial_personalizationA framework for federated learning with partial model personalization2
optimization-ai/icml2023_fedxlAn implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization.2