# This is the code for the paper 'FAVAS: Federated AVeraging for ASynchronous clients.'


### This code evaluates the method FAVAS on 3 tasks (IID tasks can be easily deduced from the non-IID ones):
- MNIST dataset with a non-IID split (see figures_mnist_noniid_n100_s20_reweight.ipynb)
- CIFAR10 dataset with a non-IID split (see figures_cifar10_noniid_n100_s20_reweight.ipynb)
- TinyImageNet dataset with an IID split (see figures_tinyimagenet_iid_n100_s20_reweight.ipynb)

(one should not forget to adapt the datasets directory paths)



#### All experiments are available with jupyter Notebooks.
Our implementation is mainly based on LUQ (https://openreview.net/forum?id=clwYez4n8e8) and QuAFL (https://github.com/ShayanTalaei/QuAFL), and adapted to our novel approach FAVAS.