Federated Reconstruction: Partially Local Federated Learning [[Paper]](https://proceedings.neurips.cc/paper/2021/hash/5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html)

PyTorch Implementation of FedRecon.

TensorFlow Implementation(Official): Repo

For simulating the Non-I.I.D scenario, the dataset is split according to Dirichlet distribution($\alpha$) by default. Furthermore, I offer another splitting method: allocating data that belong to a random subset of labels to each client.

I choose the more regular machine learning task for demonstrating algorithms, which is image classification instead of matrix factorization in the paper. And the experiment is just a toy demo, so please take it easy. 🤣

Requirements

path~=16.4.0
numpy~=1.21.2
torch~=1.10.2
fedlab~=1.1.4
torchvision~=0.11.3
rich~=12.2.0

pip install -r requirements.txt

Preprocess dataset

MNIST and CIFAR-10 is prepared.🌟

python data/preprocess.py

The way of preprocessing is adjustable, more details in data/preprocess.py.

And all splitting methods details are in data/partition

Run the experiment

Before run the experiment, please make sure that the dataset is downloaded and preprocessed already.

It’s so simple.🤪

python main.py

Hyperparameters

--global_epochs: Num of communication rounds. Default: 20

--pers_epochs: Num of local updating rounds. Default: 1

--recon_epochs: Num of local reconstruction rounds. Default: 1

--pers_lr: Learning rate used in client’s local updating phase. Default: 1e-2

--recon_lr: Learning rate used in client’s reconstruction phase. Default:1e-2

--server_lr: Learning rate used in server’s aggregation phase. Default: 1.0

--dataset: Name of experiment dataset. Default: mnist

--client_num_per_round: Num of clients that participating training at each communication round. Default: 5

--no_split: Non-zero value for considering the whole client private dataset as the support set and the query set. Default: 0

--gpu: Non-zero value for using CUDA; 0 for using CPU. Default: 1

--batch_size: Batch size of client local dataset. Default: 32.

--eval_while_training: Non-zero value for performing evaluation while in training phase. Default: 1

--valset_ratio: Percentage of validation set in client local dataset. Default: 0.1

--seed: Random seed for init model parameters and selected clients. Default: 17