FED-PUB
Federated Learning Framework
Personalized Subgraph Federated Learning framework for distributed machine learning
Official Code Repository for the paper - Personalized Subgraph Federated Learning (ICML 2023)
45 stars
2 watching
9 forks
Language: Jupyter Notebook
last commit: over 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| 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 |
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| Develops an alignment framework for federated learning with non-identical client class sets | 4 |
| A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. | 138 |
| Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
| A federated learning framework with personalized memorization using deep neural networks and k-nearest neighbors for collaborative learning of statistical models | 43 |
| A framework for federated learning with partial model personalization | 2 |
| This project enables federated learning across partially class-disjoint data with curated bilateral curation. | 11 |
| An implementation of a federated learning framework for handling data heterogeneity in decentralized settings | 38 |
| A framework for collaborative distributed machine learning in enterprise environments. | 500 |
| A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 49 |
| An unsupervised federated learning algorithm that uses cross knowledge distillation to learn meaningful data representations from local and global levels. | 69 |
| An implementation of federated learning algorithm for image classification | 50 |
| A flexible framework for distributed machine learning where participants train local models and collaboratively optimize them without sharing data | 743 |
| Numerical experiments for private federated learning with communication compression algorithms | 7 |