CausalRFF
Federated learning framework
This project develops an adaptive kernel approach to federated learning of heterogeneous causal effects.
1 stars
2 watching
0 forks
Language: Jupyter Notebook
last commit: 4 months ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 wenkehuang/fccl | A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
 wyjeong/fedweit | An implementation of Federated Continual Learning with Weighted Inter-client Transfer using TensorFlow 2. | 98 |
 haozzh/fedcr | Evaluates various methods for federated learning on different models and tasks. | 17 |
 ksreenivasan/ood_federated_learning | Researchers investigate vulnerabilities in Federated Learning systems by introducing new backdoor attacks and exploring methods to defend against them. | 64 |
 daiqing98/fedcil | An implementation of a Continual Federated Learning algorithm using Generative Replay to adapt models to new data distributions. | 27 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 154 |
 desternylin/perfed | An implementation of various federated learning algorithms with a focus on communication efficiency, robustness, and fairness. | 15 |
 kai-yue/ntk-fed | A framework for federated learning that leverages the neural tangent kernel to address statistical heterogeneity in distributed machine learning. | 3 |
 hypervoyager/pfl | An implementation of heterogeneous federated learning with parallel edge and server computation | 16 |
 zlz0414/feddar | A framework for federated representation learning with domain awareness in multi-model scenarios. | 2 |
 fangxiuwen/robust_fl | An implementation of a robust federated learning framework for handling noisy and heterogeneous clients in machine learning. | 41 |
 litian96/ditto | A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. | 137 |
 yutong-dai/fednh | An implementation of a federated learning framework for handling data heterogeneity in decentralized settings | 38 |
 xtra-computing/fedov | Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 14 |
 rong-dai/dispfl | An implementation of a personalized federated learning framework with decentralized sparse training and peer-to-peer communication protocol. | 68 |