pFedBreD_public
Federated Learning Framework
A project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models
9 stars
1 watching
2 forks
Language: Python
last commit: almost 3 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
| An implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation. | 291 | |
| Implementation of various federated learning algorithms to mitigate dimensional collapse in heterogeneous federated learning environments | 64 | |
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 | |
| A framework for collaborative distributed machine learning in enterprise environments. | 500 | |
| An implementation of a heterogenous federated learning framework using model distillation. | 150 | |
| This 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 | |
| An implementation of various federated learning algorithms with a focus on communication efficiency, robustness, and fairness. | 15 | |
| An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 | |
| A framework for non-IID federated learning via neural propagation | 6 | |
| A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. | 138 | |
| A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. | 25 | |
| An open-source repository implementing various federated learning algorithms with source code for multiple deep learning applications. | 175 | |
| An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 | |
| A flexible framework for distributed machine learning where participants train local models and collaboratively optimize them without sharing data | 743 | |
| A federated learning framework with discrepancy-aware collaboration for decentralized data training | 68 |