confidence_aware_PFL
Confidence-based PFL framework
An open-source framework implementing confidence-aware personalized federated learning via variational expectation maximization for distributed machine learning.
Confidence-aware Personalized Federated Learning via Variational Expectation Maximization [Accepted at CVPR 2023]
16 stars
2 watching
4 forks
Language: Python
last commit: over 1 year ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 ai-secure/crfl | This project presents a framework for robust federated learning against backdoor attacks. | 71 |
 fangxiuwen/robust_fl | An implementation of a robust federated learning framework for handling noisy and heterogeneous clients in machine learning. | 43 |
 garyzhang99/dm-pfl | A framework for personalized federated learning that improves shift-robustness with minimal extra training overhead | 3 |
 guopengf/auto-fedrl | A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments. | 15 |
 ganyuwang/vfl-czofo | A unified framework for improving privacy and reducing communication overhead in distributed machine learning models. | 12 |
| junyizhu-ai/surrogate_model_extension | A framework for analyzing and exploiting vulnerabilities in federated learning models using surrogate model attacks | 9 |
 hypervoyager/pfl | An implementation of heterogeneous federated learning with parallel edge and server computation | 17 |
 mmorafah/pacfl | Implementation of federated learning algorithms for distributed machine learning on private client data | 37 |
 wenkehuang/fccl | A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
 rong-dai/dispfl | An implementation of a personalized federated learning framework with decentralized sparse training and peer-to-peer communication protocol. | 72 |
 kenziyuliu/private-cross-silo-fl | This repository provides an implementation of a cross-silo federated learning framework with differential privacy mechanisms. | 25 |
 xiyuanyang45/dynamicpfl | A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 57 |
 qinbinli/moon | A framework for collaborative machine learning model training that leverages similarity between model representations to correct local training. | 267 |
 cuis15/fcfl | An implementation of Fair and Consistent Federated Learning using Python. | 20 |
 clu5/federated-conformal | A framework for incorporating uncertainty quantification into federated learning models | 10 |