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
4 stars
2 watching
0 forks
Language: Jupyter Notebook
last commit: over 3 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| hongliny/fedac-neurips20 | Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
| hongliny/fco-icml21 | This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning | 12 |
 xidongwu/federated-minimax-and-conditional-stochastic-optimization | This project presents optimization techniques for federated learning and minimax games in the context of machine learning | 1 |
 sjtu-yc/federated-submodel-averaging | An implementation of federated submodel averaging (FedSubAvg) to enable collaborative learning across decentralized devices or users. | 7 |
 lyn1874/fedpvr | An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
 hongyouc/fed-rod | Develops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks. | 14 |
 liuquande/feddg-elcfs | This project presents a framework for federated domain generalization in medical image segmentation using continuous frequency space and episodic learning. | 246 |
 harliwu/fedamd | This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
 liyipeng00/convergence | Analyzes convergence of sequential federated learning on heterogeneous data using Jupyter Notebook | 4 |
 lx10077/fedavgpy | The 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/fiss | Implementations of federated incremental semantic segmentation in PyTorch. | 34 |
 clu5/federated-conformal | A framework for incorporating uncertainty quantification into federated learning models | 10 |
 gaoliang13/feddc | Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift | 81 |
 ibm/fl-arbitrary-participation | Analyzes Federated Learning with Arbitrary Client Participation using various optimization strategies and datasets. | 4 |
 zfancy/sfat | Combating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation | 28 |