This repository is the official implementation of [Near-Optimal Collaborative Learning in Bandits] (to appear in the proceedings of NeurIPS'22).

To install requirements (Python 3.8.5, using Conda to set up a virtual environment, and pip to install packages):

conda create -n near_optimal_federated python=3.8.5
conda activate near_optimal_federated
python3 -m pip install -r requirements.txt
conda deactivate

Go to https://www.mosek.com/products/academic-licenses/ to acquire a personal academic license, and follow instructions.

• Run command

bash commands.sh

Parameters can be changed in params.py, and are overridden by the parameter values stored in a JSON file at the path provided by params_path.

To compute the corresponding complexity constants from the lower bound, run

python3 test_useful.py

• Add a new bandit by creating a new instance of class FederatedAI in file algorithms.py
• Add a new dataset by adding a few lines of code to file data.py
• Add new types of rewards by creating a new instance of class problem in file problems.py

Please refer to the paper.

All of the code is under MIT license. Everyone is most welcome to submit pull requests.