This is the demo implementation of "FedLoRE: Communication-Efficient and Personalized Edge Intelligence Framework via Federated Low-Rank Estimation" (under review), the final version of this code with other comparison frameworks will be available soon.

Please run the following commands below to install dependencies.

conda create --name fl_torch python=3.10
conda activate fl_torch
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
conda install -c pytorch torchtext
conda install numpy
conda install scikit-learn

There is a main file main.py which allows running next experiments.

Run experiments on the CIFAR10 dataset, there are 100 Non-IID clients to train the CNN model collaboratively:

   python main.py --algorithm FedLoRE --dataset Cifar10 --num_classes 10 --num_clients 100

Run experiments on the CIFAR100 dataset, there are 100 Non-IID clients to train the CNN model collaboratively:

   python main.py --algorithm FedLoRE --dataset Cifar100 --num_classes 100 --num_clients 100

TODO

The skeleton codebase in this repository was adapted from PFLib [1].

[1] https://github.com/TsingZ0/PFLlib.

Zerui Shao, Email: [email protected]