This repo contains code for the paper Adaptive Expert Models for Personalization in Federated Learning to appear in International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22).
To run the code on cifar100 with 50 clients, run the following line. Results will be saved in /save/results_cifar10.csv.
python main_fed.py --model 'cnn' --dataset 'cifar100' --n_data 100 --num_clients 50 --num_classes 100 --epochs 1500 --local_ep 3 --opt 0 --p 1.0 --gpu 0 --runs 1 --filename results_cifar10.csv
To see all options, run
python main_fed.py -h
If you want to allow for overlap between class labels, pass the argument --overlap.
You can also specify a configuration file and iterate over a parameter by running
python iterator.py --filename config_femnist.json
Note that for the FEMNIST dataset, you will have to generate the data by running the preprocessing script in the leaf/data/femnist subfolder. See FEMNIST dataset
for more information on this script.
It is possible to iterate over for example number of clusters, using configuration specified in a config file.
python iterator_clusters_old.py --explore_strategy eps --min_clusters 1 --max_clusters 6 --filename config_cifar10_small.json
See our paper.
To run this code in Docker, use the following.
In subdirectory docker run make debug or in the root directory docker run -it --rm -v pwd:/home/user/src martisak/fl-moe:latest bash
You can build this image in the docker directory with make build.
If you find this work useful, please cite us.
The code developed in this repo was was adapted from Specialized federated learning using a mixture of experts by Edvin Listo Zec which in turn was adapted from Federated Learning by Shaoxiong Ji.