episode

Federated Learning optimizer

An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance

[ICLR 2023] EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data

GitHub

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

Related projects:

RepositoryDescriptionStars
mingruiliu-ml-lab/episode_plusplusAn algorithm for Federated Learning that handles client subsampling and data heterogeneity with unbounded smoothness0
lins-lab/fedbrAn implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data25
illidanlab/splitmixAn algorithm for distributed learning with flexible model customization during training and testing40
hongliny/fedac-neurips20Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting.14
litian96/fedproxAn optimization framework designed to address heterogeneity in federated learning across distributed networks655
optimization-ai/icml2023_fedxlAn implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization.2
baowenxuan/fedcollabAn algorithm that optimizes collaboration in federated learning by clustering clients into non-overlapping coalitions based on data quantity and pairwise distribution distances.16
divyansh03/fedexpAn implementation of a federated averaging algorithm with an extrapolation approach to speed up distributed machine learning training on client-held data.9
zackzikaixiao/fedgrabA tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios.14
hmgxr128/mifa_codeAn implementation of Fast Federated Learning under device unavailability for minimizing latency and achieving optimal convergence rates9
mediabrain-sjtu/pfedgraphThis 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
harliwu/fedamdThis project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence.2
pengyang7881187/fedrlEnabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data56
omarfoq/fedemDevelops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.157
hongliny/fco-icml21This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning12