Robust-DPFL

The codes of our Robust-DPFL method.

Code Files

preprocessing.py: Functions for loading and processing the raw data of different experimental datasets, including MNIST, FEMNIST, and CIFAR10.

poison.py: Functions for posioning the data via backdoor attacks.

model.py: Functions of the deep learning model trained in experiments.

FLTrain.py: Functions of the federated learning workflow, including different attack strategies on DP-FL, and our Robust-DPFL method.

Main.py: Functions of the training and evaluation workflow.

Data Files

Data
│---MNIST: Training and test data of MNIST 
│---CIFAR10: Training and test data of CIFAR10

Data Files

Result
│--- Files for saving experimental results.

Command

Parameters

-d: --dataset, parameter for controlling the expriment datasets, legal values: MNIST, FEMNIST, CIFAR10

-a: --attack-mode, parameter for controlling the attack strategy, legal values: AttackNaive, AttackNonDP, AttackDPFL

-m: --defense-mode, parameter for controlling the federated gradient aggergation strategy, legal values: FedAvg, RobustDPFL

-t: --taxic-ratio, parameter for controlling the ratio of malicious client, legal value: a float number ranging from 0 to 1

-a: --alpha, paramter for controlling the privacy levels, legal value: a float number greater than 1

-e: --epsilon, paramter for controlling the privacy levels, legal value: a float number greater than 0

-g: --gpu, paramter for controlling the used GPU ID, legal value: a int number

Quick Command

python Main.py -d MNIST -t 0.15 -a AttackDPFL -m RobustDPFL