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

49 stars
1 watching
6 forks
Language: Python
last commit: over 2 years ago
bayesianbayesian-neural-networkfederated-learningvariational-bayesian-inference

Related projects:

Repository Description Stars
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
ignavierng/notears-admm An implementation of Bayesian network structure learning with continuous optimization for federated learning. 10
idanachituve/pfedgp An implementation of Personalized Federated Learning with Gaussian Processes using Python. 32
royson/fedl2p This project enables personalized learning models by collaborating on learning the best strategy for each client 19
jinheonbaek/fed-pub Personalized Subgraph Federated Learning framework for distributed machine learning 44
bdemo/pfedbred_public A project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models 8
mehdiset/perfedmask An implementation of personalized federated learning with optimized masking vectors using PyTorch 15
charliedinh/pfedme An implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation. 289
dawenzi098/sfl-structural-federated-learning A Python implementation of Personalized Federated Learning with Graph using PyTorch. 50
pengyang7881187/fedrl Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data 54
xiyuanyang45/dynamicpfl A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness 51
hui-po-wang/progfed An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. 20
aiot-mlsys-lab/fedrolex An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. 61
krishnap25/fl_partial_personalization A framework for federated learning with partial model personalization 2
optimization-ai/icml2023_fedxl An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. 2