fednar

FedOpt Algorithm

A Python implementation of federated optimization algorithm with normalized annealing regularization.

GitHub

6 stars
1 watching
0 forks
Language: Python
last commit: over 3 years ago

Related projects:

RepositoryDescriptionStars
unc-optimization/feddrAn implementation of algorithms for nonconvex federated learning optimization8
mc-nya/fednestAn implementation of a federated optimization algorithm for distributed machine learning6
lyn1874/fedpvrAn implementation of a federated learning algorithm for handling heterogeneous data6
divyansh03/fedexpAn implementation of a federated averaging algorithm with an extrapolation approach to speed up distributed machine learning training on client-held data.9
ignavierng/notears-admmAn implementation of Bayesian network structure learning with continuous optimization for federated learning.10
lins-lab/fedbrAn implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data25
yuetan031/fedprotoAn implementation of federated learning with prototype-based methods across heterogeneous clients134
alshedivat/fedpaA modular JAX implementation of federated learning via posterior averaging for decentralized optimization50
litian96/fedproxAn optimization framework designed to address heterogeneity in federated learning across distributed networks655
gaoliang13/feddcFederated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift81
optimization-ai/icml2023_fedxlAn implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization.2
debcaldarola/fedsamImproving generalization in federated learning by seeking flat minima through optimization techniques82
hongliny/fedac-neurips20Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting.14
lx10077/fedavgpyThe purpose of this project is to investigate the convergence of a federated learning algorithm on non-IID (non-identically and independently distributed) data.255
bibikar/feddstAn implementation of federated learning with sparse training and readjustment mechanisms to reduce communication overhead while maintaining model performance.29