This is the code accompanying the ICLR 2020 paper "Federated Learning with Matched Averaging " Paper link: [https://openreview.net/forum?id=BkluqlSFDS]
FedMA algorithm is designed for federated learning of modern neural network architectures e.g. convolutional neural networks (CNNs) and LSTMs. FedMA constructs the shared global model in a layer-wise manner by matching and averaging hidden elements (i.e. channels for convolution layers; hidden states for LSTM; neurons for fully connected layers) with similar feature extraction signatures.
Tested stable depdencises:
- python 3.6.5 (Anaconda)
- PyTorch 1.1.0
- torchvision 0.2.2
- CUDA 10.0.130
- cuDNN 7.5.1
- lapsolver 1.0.2
For the language model experiments, we used the Shakespeare dataset provided by project Leaf. Following the instructions to prepare Shakespeare dataset, we choose to use non-i.i.d., full-size dataset, and split 80% of the data points into the training dataset. Moreover, we set minimum number of samples per user at 9K. Thus, the following command returns our data partitioning:
./preprocess.sh -s niid --sf 1.0 -k 0 -t sample -tf 0.8 -k 9
We simulate a heterogeneous partition for which batch sizes and class proportions are unbalanced. We simulate a heterogeneous partition by sampling proportion of the data points in each class across participating clients from a Dirichlet distribution. Due to the small concentration parameter (0.5) of the Dirichlet distribution, some sampled batches may not have any examples
of certain classes of data. Details about this partition can be found in the partition_data function in ./utils.py.
The source code involving language task experiments i.e. LSTM over the Shakespeare dataset locates in the folder FedMA/language_modeling. And we summarize the functionality of each script below.
| Script | Functionality |
|---|---|
ensemble_accuracy_calculator.py |
Evaluating the performance of ensemble accross local models trained on paritipating clients. |
language_main.py |
Conducting FedAvg and FedProx experiments, which are used as baseline methods. |
language_oneshot_matching.py |
Evaluating the performance of one-shot match i.e. PFNM-style model fusion. |
language_whole_training.py |
Centralized training over one device i.e. we combine the local datasets and coduct centralized training. This is the strongest possible baseline for any Federated Leaarning method. |
lstm_fedma_with_comm.py |
Our proposed "FedMA with communication algorithm". |
The main result related to the image classification task i.e. VGG-9 on CIFAR-10 can be reproduced via running ./run.sh. The following arguments to the ./main.py file control the important parameters of the experiment.
| Argument | Description |
|---|---|
model |
The CNN architecture that each client train locally. |
dataset |
Dataset to use. We use CIFAR-10 to study FedMA. |
lr |
Inital learning rate that will be use. |
retrain_lr |
The learning rate for the local re-training process. Usually set to the same value as lr |
batch-size |
Batch size for the optimizers e.g. SGD or Adam. |
epochs |
Locally training epochs. |
retrain_epochs |
Local re-training epochs. |
n_nets |
Number of participating local clients. |
partition |
Data partitioning strategy. Set to hetero-dir for the simulated heterogeneous CIFAR-10 dataset. |
comm_type |
Federated learning methods. Set to fedavg, fedprox, or fedma. |
comm_round |
Number of communication rounds to use in fedavg, fedprox, and fedma. |
retrain |
Flag to retrain the model or load from checkpoint. |
rematching |
Flag to re-conduct the matching process or load from checkpoint. |
python main.py --model=moderate-cnn \
--dataset=cifar10 \
--lr=0.01 \
--retrain_lr=0.01 \
--batch-size=64 \
--epochs=20 \
--retrain_epochs=20 \
--n_nets=16 \
--partition=hetero-dir \
--comm_type=fedma \
--comm_round=50 \
--retrain=True \
--rematching=True
The results of interpretability we presented in the FedMA paper are summerized in a jupyter notebook i.e. ./jupyter_notebook/Interpretability_fedma.ipynb.
The handeling data bias experiments we presented the FedMA paper are summerized in the script ./dist_skew_main.py. To reproduce the experiment, one can simply run:
bash run_dist_skew.sh
python dist_skew_main.py --model=moderate-cnn \
--dataset=cifar10 \
--lr=0.01 \
--retrain_lr=0.01 \
--batch-size=64 \
--epochs=10 \
--retrain_epochs=20 \
--n_nets=2 \
--partition=homo \
--comm_type=fedma \
--retrain=True \
--rematching=True
@inproceedings{
Wang2020Federated,
title={Federated Learning with Matched Averaging},
author={Hongyi Wang and Mikhail Yurochkin and Yuekai Sun and Dimitris Papailiopoulos and Yasaman Khazaeni},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=BkluqlSFDS}
}
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