Official codes for ECAI '23 full paper: Unsupervised Graph Structure-Assisted Personalized Federated Learning
We provide four federated benchmark datasets spanning a wide range of machine learning tasks: handwritten character recognition(MNIST), image classification(CIFRA10), language modeling (Shakespeare) and traffic forecasting(METR-LA).
Shakespeare dataset is naturally non-i.i.d distributed where each client represents a character. For MNIST and CIFAR-10 datasets, we artificially partitioned the raw dataset using a parameter q (shards) to control the level of heterogeneity. METR-LA [35] is a traffic dataset that has a graph topology connecting sensors on roads. Each sensor on the road can be considered a client in the federated learning system, contributing data collected from real-world sources with a non-IID distribution.
The following table summarizes the datasets and models
| Dataset | Task | Model |
|---|---|---|
| MNIST | Handwritten character recognition | MCLR |
| CIFAR10 | Image classification | MobileNet-v2 |
| Shakespeare | Next character prediction | Stacked LSTM |
| METR-LA | Traffic forecasting | GRU |
See the README.md files of respective dataset, i.e. data/ for instructions on generating data.
python -u main.py --dataset Mnist-allocation_shards5-ratio1.0-u100 --algorithm FedSKA\
--batch_size 32 --tau 0.9 --k 30 --feature_hidden_size 64 --beta 0.01 --num_users 100 --learning_rate 0.01 --num_glob_iters 500 --E 1 --times 1
We provide example scripts to run paper experiments under experiments/ directory.