Sharp-Bounds-for-FedAvg-and-Continuous-Perspective

Federated Bounds

This project provides mathematical bounds on the performance of Federated Averaging algorithms with Local SGD and Continuous Perspective

GitHub

4 stars
2 watching
0 forks
Language: Jupyter Notebook
last commit: almost 5 years ago

Related projects:

RepositoryDescriptionStars
hongliny/fedac-neurips20Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting.14
hongliny/fco-icml21This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning12
xidongwu/federated-minimax-and-conditional-stochastic-optimizationThis project presents optimization techniques for federated learning and minimax games in the context of machine learning1
sjtu-yc/federated-submodel-averagingAn implementation of federated submodel averaging (FedSubAvg) to enable collaborative learning across decentralized devices or users.7
lyn1874/fedpvrAn implementation of a federated learning algorithm for handling heterogeneous data6
hongyouc/fed-rodDevelops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks.14
liuquande/feddg-elcfsThis project presents a framework for federated domain generalization in medical image segmentation using continuous frequency space and episodic learning.246
harliwu/fedamdThis project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence.2
liyipeng00/convergenceAnalyzes convergence of sequential federated learning on heterogeneous data using Jupyter Notebook4
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
jiahuadong/fissImplementations of federated incremental semantic segmentation in PyTorch.34
clu5/federated-conformalA framework for incorporating uncertainty quantification into federated learning models10
gaoliang13/feddcFederated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift81
ibm/fl-arbitrary-participationAnalyzes Federated Learning with Arbitrary Client Participation using various optimization strategies and datasets.4
zfancy/sfatCombating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation28