ma-fl-mia

Install required packages with requirements.txt. Remove wandb or disable WANDB_EXISTS in src/__init__.py to run code standalone.

Run flower_train.py for federated training, central_train.py for centralized training. Configure hyperparameters in config.json.

Model-agnostic matrix mapping in src/model_aggregation.py

Client handler is in src/flower_client.py

Server to client message handling in src/flower_strategy.py and src/model_aggregation.py

TensorFlow models are obsolete, use PyTorch instead! Model aggregation is framework-independent, but latest versions are tested only on PyTorch.

FEMNIST

Generating sample following original leaf code:

./preprocess.sh -s niid --sf 0.2 -k 300 -t user --smplseed 0 --spltseed 0

Don't forget to change data_preparation.load_data() if you're using a different sample generation.

Writers to client partitioning: [3,6,9,12,15,18,21,24,27] and rest of the clients (32 in this seed)

Example data partition: [11267, 9562, 8603, 7318, 6530, 5275, 4357, 3184, 2162, 1001]

Config

• dataset: for CIFAR10, CIFAR100, or FEMNIST dataset
• num_clients: for number of clients to train with
• rounds: to configurate number of federated rounds, epochs for the local training epochs.
• split_mode: homogen for IID distribution, balanced for class-balanced, sample size inbalanced data, dirichlet for Dirichlet distribution
• ma_mode: no for FedAvg, heterofl for HeteroFL, fjord for FjORD, rm-cid for our model-agnostic methods (including a second implementation of HeteroFL)
• unit_size: to configure size of the models. The four complexity level tested: 8,16,32,64.
• scale_mode: variations of Large client, small client ratios:
  • Experiments in paper: 0-9integers to determine the number of big clients [0,num_clients)
  • [0.0,1.0) for scaling every client down r=unit_size_client/unit_size_server (similarly to original FDropout)
  • (-num_clients,0) for unit_size_client=unit_size_server-r reduced size by integer number of channels
• permutate_cuts: repeated for Fixed and incremental for Reshampled in the paper taxonomy. one-repeated and one-incremental for one group, group-repeated and group-incremental for several groups, and repeated or incremental for unique.
• cut_type: random for Random or submatrix for Submatrix.
• cut_layerwise: for experiment with same name in Appendix

Named variations

• FDropout: {"cut_type": "random", "permutate_cuts":"incremental"}
• MaPP-FL (M): {"cut_type": "submatrix", "permutate_cuts":"group-repeated"} or {"cut_type": "submatrix", "permutate_cuts":"repeated"}
• MaPP-FL (R): {"cut_type": "random", "permutate_cuts":"group-repeated"}
• HeteroFL: {"cut_type": "submatrix", "permutate_cuts":"one-repeated"}
• HeteroFL (R): {"cut_type": "random", "permutate_cuts":"one-repeated"}

Attack with Trajectory MIA and LiRA attacks:

To generate detailed attacks with all 3 MIA (tMIA, LiRA, Yeom), one has to run the script trajectoryMIA.py. It expects a list of models to attack with the same shadow models.