jackqqwang / fedcovid

Code for the Federated COVID-19 Vaccine Side Effect Prediction

We implemented the code of FedCovid.

Files

The "data" folder contains sample training and testing data. The data sample data has been added noise due to the data sharing policy of IQVIA

The main file is federated_learning.py. Backbones are in neural_nets.py. The paremater setting and adjustment is option.py.

Requirements

• Python 3.9.7

• Pytorch 1.9.1

• Pytorch-metric-learning 1.2.1

Parameters

• define_loss = ours, our defined loss

• warmup_round = 200, different values for hyperparameter study

• active_rate = 0.4, active client ratio for each communication round

• weight_list, weights before each compoment of the designed loss

• lr_stra = 1, enable the adaptive learning rate strategy

• backbone: CNN, LSTM, Transformer, biLSTM

• aggregation: weighted_mean, mean

• strategy: central_to_all, all_state

How to run

Note: This code is written in Python3.

python federated_learning.py