PF_MAB

Federated MAB algorithm

An implementation of Federated Multi-Armed Bandits with Personalization using Python and Jupyter Notebook.

GitHub

6 stars
1 watching
4 forks
Language: Jupyter Notebook
last commit: almost 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 42
jinheonbaek/fed-pub Personalized Subgraph Federated Learning framework for distributed machine learning 44
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 9
jiahuadong/fiss Implementations of federated incremental semantic segmentation in PyTorch. 33
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 An implementation of personalized federated learning with optimized masking vectors using PyTorch 15
pengyang7881187/fedrl Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data 54
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 133
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 efficient method for federated clustering and its corresponding unlearning procedure to provably achieve accurate results 18