This repository is to provide code and data

Supplementry data viewer - https://mia-fl.github.io/MIA-FL/

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Code Handbook

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File structures

Backbone files

These files provides the core functionality in the experiment setting

1. aggregator.py - The aggregator collects and calculate the gradients from participants
2. constants.py - All hyper-parameters
3. data_reader.py - The module loading data from data set files and distribute them to participants
4. models.py - The participants, global model, and local attackers
5. organizer.py - The module setting up different experiments

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Experiment runnable

These files are the runnable experiment files calling above backbone files

1. blackbox_agr_op.py
2. blackbox_agr_optimized.py
3. blackbox_baseline.py
4. blackbox_optimized.py
5. blackbox_starting_baseline.py
6. grey1_baseline_texas_trmean.py
7. greybox_I_baseline_misleading.py
8. greybox_I_baseline.py
9. greybox_II_baseline.py
10. greybox1_starting_baseline.py
11. optimized_greybox1.py
12. whitebox_global_non_target_starting_baseline.py
13. whitebox_global_non_targeted_baseline.py
14. whitebox_global_target_starting_baseline.py
15. whitebox_global_targeted_round_robbin_shadow_ver.py
16. whitebox_global_targeted_round_robbin_starting_point_baseline.py
17. whitebox_global_targeted_round_robbin.py
18. whitebox_local_baseline.py
19. whitebox_local_optimized.py
20. whitebox_local_targeted_baseline.py

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Setup

The dataset_purchase.tgz need to be extracted as 'dataset_purchase' before running

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Constant List

Key constants

1. DEFAULT_SET
   • The chosen dataset to run experiments
   • Available values
     1. PURCHASE100
     2. CIFAR_10
     3. LOCATION30
     4. TEXAS100
     5. GNOME
2. DEFAULT_AGR
   • The chosen robust aggregation mechanisms to defend malicious updates
   • Available values
     1. TRMEAN = "Trimmed Mean"
     2. KRUM = "Krum"
     3. MULTI_KRUM = "Multi-Krum"
     4. MEDIAN = "Median"
     5. FANG = "Fang"
     6. None (Please use the python reserved None value)
3. NUMBER_OF_PARTICIPANTS: The number of participants involved in federated learning process
4. NUMBER_OF_ADVERSARY: The number of adversary involved in attack experiments, the adversary is in a DISJOINT set with participants
5. MAX_EPOCH: The overall rounds of federated training for the current experiment
6. TRAIN_EPOCH: The overall rounds of deferated training without malicious updates. In other words, the number of training rounds that the adversary waits the model to converge
7. EXPERIMENTAL_DATA_DIRECTORY: The directory to store experiment data, it must be a existing directory
8. GLOBAL_SEED: The random seed used