PF_MAB
Federated MAB algorithm
An implementation of Federated Multi-Armed Bandits with Personalization using Python and Jupyter Notebook.
6 stars
1 watching
4 forks
Language: Jupyter Notebook
last commit: about 3 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| shengroup/fmab | Federated Multi-armed Bandits algorithm implementation for simulating cognitive radio systems and recommender systems | 9 |
 shenzebang/federated-learning-pytorch | A PyTorch-based framework for Federated Learning experiments | 40 |
 chunmeifeng/fedpr | An algorithm for learning federated visual prompts in null space to improve MRI reconstruction performance on limited local data and reduced communication costs | 45 |
 jinheonbaek/fed-pub | Personalized Subgraph Federated Learning framework for distributed machine learning | 45 |
 mc-nya/fednest | An implementation of a federated optimization algorithm for distributed machine learning | 6 |
 yamingguo98/fediir | An implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships | 10 |
 jiahuadong/fiss | Implementations of federated incremental semantic segmentation in PyTorch. | 34 |
 mediabrain-sjtu/pfedgraph | 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 |
 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 |
 mehdiset/perfedmask | A PyTorch implementation of personalized federated learning with optimized masking vectors for various machine learning tasks and datasets. | 15 |
 pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
 tfzhou/fedfa | An ICLR 2023 paper implementation in PyTorch of Federated Feature Augmentation for federated learning with data augmentation and medical image analysis. | 57 |
 yuetan031/fedproto | An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 |
 hui-po-wang/progfed | An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 |
 thupchnsky/mufc | An implementation of a federated clustering approach with an unlearning mechanism to improve data privacy and security in distributed machine learning | 19 |