FedBR

Federated Learning Algorithm

An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data

[ICML 2023] FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction

GitHub

25 stars
3 watching
1 forks
Language: Python
last commit: 9 months ago

Related projects:

Repository Description Stars
lunanbit/fedul This project presents an approach to federated learning that leverages unsupervised techniques to adapt models to unlabeled data without requiring labels. 33
lyn1874/fedpvr An implementation of a federated learning algorithm for handling heterogeneous data 6
lins-lab/fedthe Improves machine learning models for personalized performance under evolving test distributions in distributed environments 53
mingruiliu-ml-lab/episode An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance 2
hongliny/fedac-neurips20 Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. 14
ignavierng/notears-admm An implementation of Bayesian network structure learning with continuous optimization for federated learning. 10
optimization-ai/icml2023_fedxl An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. 2
sungwon-han/fedx An unsupervised federated learning algorithm that uses cross knowledge distillation to learn meaningful data representations from local and global levels. 68
gaoliang13/feddc Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift 79
mediabrain-sjtu/fedgela Federated learning algorithm designed to handle partially class-disjoint data by utilizing bilateral curation and Dirichlet partitioning. 10
mingruiliu-ml-lab/episode_plusplus An algorithm for Federated Learning that handles client subsampling and data heterogeneity with unbounded smoothness 0
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
pengyang7881187/fedrl Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data 54
xtra-computing/fedov Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. 14
litian96/fair_flearn This project develops and evaluates algorithms for fair resource allocation in federated learning, aiming to promote more inclusive AI systems. 243