QSFL
FL optimizer
An optimization framework for federated learning
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning (ICML'22)
12 stars
2 watching
0 forks
Language: Python
last commit: over 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
optimization-ai/icml2023_fedxl | An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
litian96/fedprox | An optimization framework designed to address heterogeneity in federated learning across distributed networks | 652 |
omarfoq/communication-in-cross-silo-fl | A toolkit for optimizing federated learning in cross-silo settings by designing efficient communication topologies | 30 |
hongliny/fco-icml21 | This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning | 11 |
ibm/reprogrammble-fl | Improves utility-privacy tradeoff in federated learning by reprogramming models to balance data utility and user privacy. | 5 |
cuis15/fcfl | An implementation of Fair and Consistent Federated Learning using Python. | 20 |
hui-po-wang/progfed | An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 |
guopengf/auto-fedrl | A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments. | 15 |
zackzikaixiao/fedgrab | A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. | 13 |
dawenzi098/sfl-structural-federated-learning | A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 50 |
xidongwu/federated-minimax-and-conditional-stochastic-optimization | This project presents optimization techniques for federated learning and minimax games in the context of machine learning | 0 |
mingruiliu-ml-lab/episode | An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |
omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 155 |
xiyuanyang45/dynamicpfl | A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 55 |
gwenlegate/guidinglastlayerflpretrain | Investigates transfer learning in federated learning by guiding the last layer with pre-trained models | 7 |