SFAT

Heterogeneity fixer

Combating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation

[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"

GitHub

28 stars
2 watching
7 forks
Language: Python
last commit: over 1 year ago
adversarial-learningdata-heterogeneityfederated-adversarial-learningfederated-learningrobust-machine-learning

Related projects:

Repository Description Stars
pengyang7881187/fedrl Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data 54
zackzikaixiao/fedgrab A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. 13
mmendiet/fedalign A federated learning framework designed to mitigate data heterogeneity in distributed learning settings. 55
aiot-mlsys-lab/fedrolex An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. 61
dawenzi098/sfl-structural-federated-learning A Python implementation of Personalized Federated Learning with Graph using PyTorch. 50
lyn1874/fedpvr An implementation of a federated learning algorithm for handling heterogeneous data 6
zhenqincn/fedapen An implementation of cross-silo federated learning with adaptability to statistical heterogeneity 10
omarfoq/fedem Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. 154
haozzh/fedcr Evaluates various methods for federated learning on different models and tasks. 17
yamingguo98/fediir An implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships 9
yuetan031/fedproto An implementation of federated learning with prototype-based methods across heterogeneous clients 133
xtra-computing/fedov Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. 14
xiyuanyang45/dynamicpfl A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness 51
diaoenmao/heterofl-computation-and-communication-efficient-federated-learning-for-heterogeneous-clients An implementation of efficient federated learning algorithms for heterogeneous clients 152
alshedivat/fedpa A modular JAX implementation of federated learning via posterior averaging for decentralized optimization 49