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FS-TFP/federatedscope/core/configs/README.md

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Configurations

We summarize all the customizable configurations:

config

The configurations related to environment of running experiment.

Name (Type) Default Value Description Note
backend (string) 'torch' The backend for local training -
use_gpu (bool) False Whether to use GPU -
check_completeness (bool) False Whether to check the completeness of msg_handler -
verbose (int) 1 Whether to print verbose logging info -
print_decimal_digits (int) 6 How many decimal places we print out using logger -
device (int) -1 Specify the device for training -
seed (int) 0 Random seed -
outdir (string) '' The dir used to save log, exp_config, models, etc,. -
expname (string) '' Detailed exp name to distinguish different sub-exp -
expname_tag (string) '' Detailed exp tag to distinguish different sub-exp with the same expname -

Data

The configurations related to the data/dataset are defined in cfg_data.py.

Name (Type) Default Value Description Note
data.root (string) 'data' The folder where the data file located. data.root would be used together with data.type to load the dataset. -
data.type (string) 'toy' Dataset name CV: 'femnist', 'celeba' ; NLP: 'shakespeare', 'subreddit', 'twitter'; Graph: 'cora', 'citeseer', 'pubmed', 'dblp_conf', 'dblp_org', 'csbm', 'epinions', 'ciao', 'fb15k-237', 'wn18', 'fb15k' , 'MUTAG', 'BZR', 'COX2', 'DHFR', 'PTC_MR', 'AIDS', 'NCI1', 'ENZYMES', 'DD', 'PROTEINS', 'COLLAB', 'IMDB-BINARY', 'IMDB-MULTI', 'REDDIT-BINARY', 'IMDB-BINARY', 'IMDB-MULTI', 'HIV', 'ESOL', 'FREESOLV', 'LIPO', 'PCBA', 'MUV', 'BACE', 'BBBP', 'TOX21', 'TOXCAST', 'SIDER', 'CLINTOX', 'graph_multi_domain_mol', 'graph_multi_domain_small', 'graph_multi_domain_mix', 'graph_multi_domain_biochem'; MF: 'vflmovielens1m', 'vflmovielens10m', 'hflmovielens1m', 'hflmovielens10m', 'vflnetflix', 'hflnetflix'; Tabular: 'toy', 'synthetic'; External dataset: 'DNAME@torchvision', 'DNAME@torchtext', 'DNAME@huggingface_datasets', 'DNAME@openml'.
data.file_path (string) '' The path to the data file, only makes effect when data.type = 'file' -
data.args (list) [] Args for the external dataset Used for external dataset, eg. [{'download': False}]
data.save_data (bool) False Whether to save the generated toy data -
data.splitter (string) '' Splitter name for standalone dataset Generic splitter: 'lda'; Graph splitter: 'louvain', 'random', 'rel_type', 'graph_type', 'scaffold', 'scaffold_lda', 'rand_chunk'
data.splitter_args (list) [] Args for splitter. Used for splitter, eg. [{'alpha': 0.5}]
data.transform (list) [] Transform for x of data Used in get_item in torch.dataset, eg. [['ToTensor'], ['Normalize', {'mean': [0.9637], 'std': [0.1592]}]]
data.target_transform (list) [] Transform for y of data Use as data.transform
data.pre_transform (list) [] Pre_transform for torch_geometric dataset Use as data.transform
dataloader.batch_size (int) 64 batch_size for DataLoader -
dataloader.drop_last (bool) False Whether drop last batch (if the number of last batch is smaller than batch_size) in DataLoader -
dataloader.sizes (list) [10, 5] Sample size for graph DataLoader The length of dataloader.sizes must meet the layer of GNN models.
dataloader.shuffle (bool) True Shuffle train DataLoader -
data.server_holds_all (bool) False Only use in global mode, whether the server (workers with idx 0) holds all data, useful in global training/evaluation case -
data.subsample (float) 1.0  Only used in LEAF datasets, subsample clients from all clients -
data.splits (list) [0.8, 0.1, 0.1] Train, valid, test splits -
data.
consistent_label_distribution		 (bool) True Make label distribution of train/val/test set over clients keep consistent during splitting -
data.cSBM_phi (list) [0.5, 0.5, 0.5] Phi for cSBM graph dataset -
data.loader (string) '' Graph sample name, used in minibatch trainer 'graphsaint-rw': use GraphSAINTRandomWalkSampler as DataLoader; 'neighbor': use NeighborSampler as DataLoader.
dataloader.num_workers (int) 0 num_workers in DataLoader -
dataloader.walk_length (int) 2 The length of each random walk in graphsaint. -
dataloader.num_steps (int) 30 The number of iterations per epoch in graphsaint. -
data.quadratic.dim (int) 1 Dim of synthetic quadratic  dataset -
data.quadratic.min_curv (float) 0.02 Min_curve of synthetic quadratic dataset -
data.quadratic.max_curv (float) 12.5 Max_cur of synthetic quadratic dataset -

Model

The configurations related to the model are defined in cfg_model.py.
| General | Criterion | Regularization |

Model-General

Name (Type) Default Value Description Note
model.
model_num_per_trainer		 (int) 1 Number of model per trainer some methods may leverage more
model.type (string) 'lr' The model name used in FL CV: 'convnet2', 'convnet5', 'vgg11', 'lr'; NLP: 'LSTM', 'MODEL@transformers'; Graph: 'gcn', 'sage', 'gpr', 'gat', 'gin', 'mpnn'; Tabular: 'mlp', 'lr', 'quadratic'; MF: 'vmfnet', 'hmfnet'
model.use_bias (bool) True Whether use bias in lr model -
model.task (string) 'node' The task type of model, the default is Classification NLP: 'PreTraining', 'QuestionAnswering', 'SequenceClassification', 'TokenClassification', 'Auto', 'WithLMHead'; Graph: 'NodeClassification', 'NodeRegression', 'LinkClassification', 'LinkRegression', 'GraphClassification', 'GraphRegression',
model.hidden (int) 256 Hidden layer dimension -
model.dropout (float) 0.5 Dropout ratio -
model.in_channels (int) 0 Input channels dimension If 0, model will be built by data.shape
model.out_channels (int) 1 Output channels dimension -
model.layer (int) 2 Model layer -
model.graph_pooling (string) 'mean' Graph pooling method in graph-level task 'add', 'mean' or 'max'
model.embed_size (int) 8 embed_size in LSTM -
model.num_item (int) 0 Number of items in MF. It will be overwritten by the real value of the dataset.
model.num_user (int) 0 Number of users in MF. It will be overwritten by the real value of the dataset.

Criterion

Name (Type) Default Value Description Note
criterion.type (string) 'MSELoss' Criterion type Chosen from https://pytorch.org/docs/stable/nn.html#loss-functions , eg. 'CrossEntropyLoss', 'L1Loss', etc.

Regularizer

Name (Type) Default Value Description Note
regularizer.type (string) ' ' The type of the regularizer Chosen from [proximal_regularizer]
regularizer.mu (float) 0 The factor that controls the loss of the regularization term -

Federated Algorithms

The configurations related to specific federated algorithms, which are defined in cfg_fl_algo.py.

| FedOPT | FedProx | personalization | fedsageplus | gcflplus | flitplus |

fedopt: for FedOpt algorithm

Name (Type) Default Value Description Note
fedopt.use (bool) False Whether to run FL courses with FedOpt algorithm. If False, all the related configurations (cfg.fedopt.xxx) would not take effect.
fedopt.optimizer.type (string) 'SGD' The type of optimizer used for FedOpt algorithm. Currently we support all optimizers build in PyTorch (The modules under torch.optim).
fedopt.optimizer.lr (float) 0.1 The learning rate used in for FedOpt optimizer. -

fedprox: for FedProx algorithm

Name (Type) Default Value Description Note
fedprox.use (bool) False Whether to run FL courses with FedProx algorithm. If False, all the related configurations (cfg.fedprox.xxx) would not take effect.
fedprox.mu (float) 0.0 The hyper-parameter \mu used in FedProx algorithm. -

personalization: for personalization algorithms

Name (Type) Default Value Description Note
personalization.local_param (list of str) [] The client-distinct local param names, e.g., ['pre', 'bn'] -
personalization.
share_non_trainable_para		 (bool) False Whether transmit non-trainable parameters between FL participants -
personalization.
local_update_steps		 (int) -1 The local training steps for personalized models By default, -1 indicates that the local model steps will be set to be the same as the valid train.local_update_steps
personalization.regular_weight (float) 0.1 The regularization factor used for model para regularization methods such as Ditto and pFedMe. The smaller the regular_weight is, the stronger emphasising on personalized model.
personalization.lr (float) 0.0 The personalized learning rate used in personalized FL algorithms. The default value 0.0 indicates that the value will be set to be the same as train.optimizer.lr in case of users have not specify a valid personalization.lr
personalization.K (int) 5 The local approximation steps for pFedMe. -
personalization.beta (float) 5 The average moving parameter for pFedMe. -

fedsageplus: for fedsageplus algorithm

Name (Type) Default Value Description Note
fedsageplus.num_pred (int) 5 Number of nodes generated by the generator -
fedsageplus.gen_hidden (int) 128 Hidden layer dimension of generator -
fedsageplus.hide_portion (float) 0.5 Hide graph portion -
fedsageplus.fedgen_epoch (int) 200 Federated training round for generator -
fedsageplus.loc_epoch (int) 1 Local pre-train round for generator -
fedsageplus.a (float) 1.0 Coefficient for criterion number of missing node -
fedsageplus.b (float) 1.0 Coefficient for criterion feature -
fedsageplus.c (float) 1.0 Coefficient for criterion classification -

gcflplus: for gcflplus algorithm

Name (Type) Default Value Description Note
gcflplus.EPS_1 (float) 0.05 Bound for mean_norm -
gcflplus.EPS_2 (float) 0.1 Bound for max_norm -
gcflplus.seq_length (int) 5 Length of the gradient sequence -
gcflplus.standardize (bool) False Whether standardized dtw_distances -

flitplus: for flitplus algorithm

Name (Type) Default Value Description Note
flitplus.tmpFed (float) 0.5 gamma in focal loss (Eq.4) -
flitplus.lambdavat (float) 0.5 lambda in phi (Eq.10) -
flitplus.factor_ema (float) 0.8 beta in omega (Eq.12) -
flitplus.weightReg (float) 1.0 balance lossLocalLabel and lossLocalVAT -

Federated training

The configurations related to federated training are defined in cfg_training.py. Considering it's infeasible to list all the potential arguments for optimizers and schedulers, we allow the users to add new parameters directly under the corresponding namespace. For example, we haven't defined the argument train.optimizer.weight_decay in cfg_training.py, but the users are allowed directly use it. If the optimizer doesn't require the argument named weight_decay, an error will be raised.

| Local Training | Finetune | Grad Clipping | Early Stop |

Local training

The following configurations are related to the local training.

Name (Type) Default Value Description Note
train.local_update_steps (int) 1 The number of local training steps. -
train.batch_or_epoch (string) 'batch' The type of local training. train.batch_or_epoch specifies the unit that train.local_update_steps adopts. All new parameters will be used as arguments for the chosen optimizer.
train.optimizer - - You can add new parameters under train.optimizer according to the optimizer, e.g., you can set momentum by cfg.train.optimizer.momentum.
train.optimizer.type (string) 'SGD' The type of optimizer used in local training. Currently we support all optimizers build in PyTorch (The modules under torch.optim).
train.optimizer.lr (float) 0.1 The learning rate used in the local training. -
train.scheduler - - Similar with train.optimizer, you can add new parameters as you need, e.g., train.scheduler.step_size=10. All new parameters will be used as arguments for the chosen scheduler.
train.scheduler.type (string) '' The type of the scheduler used in local training Currently we support all schedulers build in PyTorch (The modules under torch.optim.lr_scheduler).

Fine tuning

The following configurations are related to the fine tuning.

Name (Type) Default Value Description Note
finetune.before_eval (bool) False Indicator of fintune before evaluation If True, the clients will fine tune its model before each evaluation. Note the fine tuning is only conducted before evaluation and won't influence the upload weights in each round.
finetune.local_update_steps (int) 1 The number of local fine tune steps -
finetune.batch_or_epoch (string) batch The type of local fine tuning. Similar with train.batch_or_epoch, finetune.batch_or_epoch specifies the unit of finetune.local_update_steps
finetune.optimizer - - You can add new parameters under finetune.optimizer according to the type of optimizer. All new parameters will be used as arguments for the chosen optimizer.
finetune.optimizer.type (string) 'SGD' The type of the optimizer used in fine tuning. Currently we support all optimizers build in PyTorch (The modules under torch.optim).
finetune.optimizer.lr (float) 0.1 The learning rate used in local fine tuning -
finetune.scheduler - - Similar with train.scheduler, you can add new parameters as you need, and all new parameters will be used as arguments for the chosen scheduler.

Grad Clipping

The following configurations are related to the grad clipping.

Name (Type) Default Value Description Note
grad.grad_clip (float) -1.0 The threshold used in gradient clipping. grad.grad_clip < 0 means we don't clip the gradient.

Early Stop

Name (Type) Default Value Description Note
early_stop.patience (int) 5 How long to wait after last time the monitored metric improved. Note that the actual_checking_round = early_step.patience * eval.freq. To disable the early stop, set the early_stop.patience <=0
early_stop.delta (float) 0. Minimum change in the monitored metric to indicate a improvement. -
early_stop.improve_indicaator_mode (string) 'best' Early stop when there is no improvement within the last early_step.patience rounds, in ['mean', 'best'] Chosen from 'mean' or 'best'

FL Setting

The configurations related to FL settings are defined in cfg_fl_setting.py.

| General | Distribute | Vertical |

federate: general fl setting

Name (Type) Default Value Description Note
federate.client_num (int) 0 The number of clients that involves in the FL courses. It can set to 0 to automatically specify by the partition of dataset.
federate.sample_client_num (int) -1 The number of sampled clients in each training round. -
federate.sample_client_rate (float) -1.0 The ratio of sampled clients in each training round. -
federate.unseen_clients_rate (float) 0.0 The ratio of clients served as unseen clients, which would not be used for training and only for evaluation. -
federate.total_round_num (int) 50 The maximum training round number of the FL course. -
federate.mode (string) 'standalone'
Choices: {'standalone', 'distributed'}		 The running mode of the FL course. -
federate.share_local_model (bool) False If True, only one model object is created in the FL course and shared among clients for efficient simulation. -
federate.data_weighted_aggr (bool) False If True, the weight of aggregator is the number of training samples in dataset. -
federate.online_aggr (bool) False If True, an online aggregation mechanism would be applied for efficient simulation. -
federate.make_global_eval (bool) False If True, the evaluation would be performed on the server's test data, otherwise each client would perform evaluation on local test set and the results would be merged. -
federate.use_diff (bool) False If True, the clients would return the variation in local training (i.e., $\delta$) instead of the updated models to the server for federated aggregation. -
federate.merge_test_data (bool) False If True, clients' test data would be merged and perform global evaluation for efficient simulation. -
federate.method (string) 'FedAvg' The method used for federated aggregation. We support existing federated aggregation algorithms (such as 'FedAvg/FedOpt'), 'global' (centralized training), 'local' (isolated training), personalized algorithms ('Ditto/pFedMe/FedEM'), and allow developer to customize.
federate.ignore_weight (bool) False If True, the model updates would be averaged in federated aggregation. -
federate.use_ss (bool) False If True, additively secret sharing would be applied in the FL course. Only used in vanilla FedAvg in this version.
federate.restore_from (string) '' The checkpoint file to restore the model. -
federate.save_to (string) '' The path to save the model. -
federate.join_in_info (list of string) [] The information requirements (from server) for joining in the FL course. We support 'num_sample/client_resource' and allow user customization.
federate.sampler (string) 'uniform'
Choices: {'uniform', 'group'}		 The sample strategy of server used for client selection in a training round. -
federate.
resource_info_file		 (string) '' the device information file to record computation and communication ability -
federate.process_num (int) 1 The number of parallel processes. It only takes effect when use_gpu=True, backend='torch', federate.mode='standalone' and federate.share_local_model=False, and the value is required to be not greater than the number of GPUs. -

distribute: for distribute mode

Name (Type) Default Value Description Note
distribute.use (bool) False Whether to run FL courses with distribute mode. If False, all the related configurations (cfg.distribute.xxx) would not take effect.
distribute.server_host (string) '0.0.0.0' The host of server's ip address for communication -
distribute.server_port (string) 50050 The port of server's ip address for communication -
distribute.client_host (string) '0.0.0.0' The host of client's ip address for communication -
distribute.client_port (string) 50050 The port of client's ip address for communication -
distribute.role (string) 'client'
Choices: {'server', 'client'}		 The role of the worker -
distribute.data_idx (int) -1 It is used to specify the data index in distributed mode when adopting a centralized dataset for simulation (formatted as {data_idx: data/dataloader}). data_idx=-1 means that the entire dataset is owned by the participant. And we randomly sample the index in simulation for other invalid values excepted for -1.
distribute.
grpc_max_send_message_length		 (int) 100 * 1024 * 1024 The maximum length of sent messages -
distribute.
grpc_max_receive_message_length		 (int) 100 * 1024 * 1024 The maximum length of received messages -
distribute.grpc_enable_http_proxy (bool) False Whether to enable http proxy -

vertical: for vertical federated learning

Name (Type) Default Value Description Note
vertical.use (bool) False Whether to run vertical FL. If False, all the related configurations (cfg.vertical.xxx) would not take effect.
vertical.encryption (string) paillier The encryption algorithms used in vertical FL. -
vertical.dims (list of int) [5,10] The dimensions of the input features for participants. -
vertical.key_size (int) 3072 The length (bit) of the public keys. -

Evaluation

The configurations related to monitoring and evaluation, which are adefined in cfg_evaluation.py.

| General | WandB |

Evaluation General

Name (Type) Default Value Description Note
eval.freq (int) 1 The frequency we conduct evaluation. -
eval.metrics (list of str) [] The names of adopted evaluation metrics. By default, we calculate the ['loss', 'avg_loss', 'total'], all the supported metric can be find in core/monitors/metric_calculator.py
eval.split (list of str) ['test', 'val'] The data splits' names we conduct evaluation. -
eval.report (list of str) ['weighted_avg', 'avg', 'fairness', 'raw'] The results reported forms to loggers By default, we report comprehensive results, - weighted_avg and avg indicate the weighted average and uniform average over all evaluated clients; - fairness indicates report fairness-related results such as individual performance and std across all evaluated clients; - raw indicates that we save and compress all clients' individual results without summarization, and users can flexibly post-process the saved results further.
eval.
best_res_update_round_wise_key		 (str) 'val_loss' The metric name we used to as the primary key to check the performance improvement at each evaluation round. -
eval.monitoring (list of str) [] Extended monitoring methods or metric, e.g., 'dissim' for B-local dissimilarity -
eval.count_flops (bool) True Whether to count the flops during the FL courses. -

wandb: for wandb tracking and visualization

Name (Type) Default Value Description Note
wandb.use (bool) False Whether to use wandb to track and visualize the FL dynamics and results. If False, all the related configurations (wandb.xxx) would not take effect.
wandb.name_user (str) '' the user name used in wandb management -
wandb.name_project (str) '' the project name used in wandb management -
wandb.online_track (bool) True whether to track the results in an online manner, i.e., log results at every evaluation round -
wandb.client_train_info (bool) True whether to track the training info of clients -

Asynchronous Training Strategies

The configurations related to applying asynchronous training strategies in FL are defined in cfg_asyn.py.

Name (Type) Default Value Description Note
asyn.use (bool) False Whether to use asynchronous training strategies. If False, all the related configurations (cfg.asyn.xxx) would not take effect.
asyn.time_budget (int/float) 0 The predefined time budget (seconds) for each training round. time_budget<=0 means the time budget is not applied.
asyn.min_received_num (int) 2 The minimal number of received feedback for the server to trigger federated aggregation. -
asyn.min_received_rate (float) -1.0 The minimal ratio of received feedback w.r.t. the sampled clients for the server to trigger federated aggregation. -
asyn.staleness_toleration (int) 0 The threshold of the tolerable staleness in federated aggregation. -
asyn.
staleness_discount_factor		 (float) 1.0 The discount factor for the staled feedback in federated aggregation. -
asyn.aggregator (string) 'goal_achieved'
Choices: {'goal_achieved', 'time_up'}		 The condition for federated aggregation. 'goal_achieved': perform aggregation when the defined number of feedback has been received; 'time_up': perform aggregation when the allocated time budget has been run out.
asyn.broadcast_manner (string) 'after_aggregating'
Choices: {'after_aggregating', 'after_receiving'}		 The broadcasting manner of server. 'after_aggregating': broadcast the up-to-date global model after performing federated aggregation; 'after_receiving': broadcast the up-to-date global model after receiving the model update from clients.
asyn.overselection (bool) False Whether to use the overselection technique -

Differential Privacy

| NbAFL | SGDMF |

NbAFL

The configurations related to NbAFL method.

Name (Type) Default Value Description Note
nbafl.use (bool) False The indicator of the NbAFL method. -
nbafl.mu (float) 0. The argument \mu in NbAFL. -
nbafl.epsilon (float) 100. The $\epsilon$-DP guarantee used in NbAFL. -
nbafl.w_clip (float) 1. The threshold used for weight clipping. -
nbafl.constant (float) 30. The constant used in NbAFL. -

SGDMF

The configurations related to SGDMF method (only used in matrix factorization tasks).

Name (Type) Default Value Description Note
sgdmf.use (bool) False The indicator of the SGDMF method.
sgdmf.R (float) 5. The upper bound of rating. -
sgdmf.epsilon (float) 4. The \epsilon used in DP. -
sgdmf.delta (float) 0.5 The \delta used in DP. -
sgdmf.constant (float) 1. The constant in SGDMF -
dagaloader.theta (int) -1 - -1 means per-rating privacy, otherwise per-user privacy

Auto-tuning Components

These arguments are exposed for customizing our provided auto-tuning components.

| General | SHA | FedEx | Wrappers for FedEx |

Auto-tunning General

Name (Type) Default Value Description Note
hpo.working_folder (string) 'hpo' Save model checkpoints and search space configurations to this folder. Trials in the next stage of an iterative HPO algorithm can restore from the checkpoints of their corresponding last trials.
hpo.ss (string) 'hpo' File path of the .yaml that specifying the search space. -
hpo.num_workers (int) 0 The number of threads to concurrently attempt different hyperparameter configurations. Multi-threading is banned in current version.
hpo.init_cand_num (int) 16 The number of initial hyperparameter configurations sampled from the search space. -
hpo.larger_better (bool) False The indicator of whether the larger metric is better. -
hpo.scheduler (string) 'rs'
Choices: {'rs', 'sha', 'wrap_sha'}		 Which algorithm to use. -
hpo.metric (string) 'client_summarized_weighted_avg.val_loss' Metric to be optimized. -

Successive Halving Algorithm (SHA)

Name (Type) Default Value Description Note
hpo.sha.elim_rate (int) 3 Reserve only top 1/hpo.sha.elim_rate hyperparameter configurations in each state. -
hpo.sha.budgets (list of int) [] Budgets for each SHA stage. -

FedEx

Name (Type) Default Value Description Note
hpo.fedex.use (bool) False Whether to use FedEx. -
hpo.fedex.ss (striing) '' Path of the .yaml specifying the search space to be explored. -
hpo.fedex.flatten_ss (bool) True Whether the search space has been flattened. -
hpo.fedex.eta0 (float) -1.0 Initial learning rate. -1.0 means automatically determine the learning rate based on the size of search space.
hpo.fedex.sched (string) 'auto'
Choices: {'auto', 'adaptive', 'aggressive', 'constant', 'scale' }		 The strategy to update step sizes -
hpo.fedex.cutoff (float) 0.0 The entropy level below which to stop updating the config. -
hpo.fedex.gamma (float) 0.0 The discount factor; 0.0 is most recent, 1.0 is mean. -
hpo.fedex.diff (bool) False Whether to use the difference of validation losses before and after the local update as the reward signal. -

Wrappers for FedEx

Name (Type) Default Value Description Note
hpo.table.eps (float) 0.1 The probability to make local perturbation. Larger values lead to drastically different arms of the bandit FedEx attempts to solve.
hpo.table.num (int) 27 The number of arms of the bandit FedEx attempts to solve. -
hpo.table.idx (int) 0 The key (i.e., name) of the hyperparameter wrapper considers. No need to change this argument.

Attack

The configurations related to the data/dataset are defined in cfg_attack.py.

| Privacy Attack | Back-door Attack |

For Privacy Attack

Name (Type) Default Value Description Note
attack.attack_method (str) '' Attack method name Choices: {'gan_attack', 'GradAscent', 'PassivePIA', 'DLG', 'IG', 'backdoor'}
attack.target_label_ind (int) -1 The target label to attack Used in class representative attack (GAN based method) and back-door attack; defult -1 means no label to target
attack.attacker_id (int) -1 The id of the attack client Default -1 means no client as attacker; Used in both privacy attack and back-door attack when client is the attacker
attack.reconstruct_lr (float) 0.01 The learning rate of the optimization based training data/label inference attack -
attack.reconstruct_optim (str) 'Adam' The learning rate of the optimization based training data/label inference attack Choices: {'Adam', 'SGD', 'LBFGS'}
attack.info_diff_type (str) 'l2' The distance to compare the ground-truth info (gradients or model updates) and the info generated by the dummy data. Options: 'l2', 'l1', 'sim' representing L2, L1 and cosin similarity
attack.max_ite (int) 400 The maximum iteration of the optimization based training data/label inference attack -
attack.alpha_TV (float) 0.001 The hyperparameter of the total variance term Used in the mehtod invert gradint
attack.inject_round (int) 0 The round to start performing the attack actions -
attack.classifier_PIA (str) 'randomforest' The property inference classifier name -
attack.mia_simulate_in_round (int) 20 The round to add the target data into training batch Used When simulate the case that the target data are in the training set
attack. mia_is_simulate_in (bool) False whether simulate the case that the target data are in the training set

For Back-door Attack

Name (Type) Default Value Description Note
attack.edge_path (str) 'edge_data/' The folder where the ood data used by edge-case backdoor attacks located -
attack.trigger_path (str) 'trigger/' The folder where the trigger pictures used by pixel-wise backdoor attacks located -
attack.setting (str) 'fix' The setting about how to select the attack client. Choices:{'fix', 'single', and 'all'}, 'single' setting means the attack client can be only selected in the predefined round (cfg.attack.insert_round). 'all' setting means the attack client can be selected in all round. 'fix' setting means that the attack client can be selected every freq round. freq has beed defined in the cfg.attack.freq keyword.
attack.freq (int) 10 This keyword is used in the 'fix' setting. The attack client can be selected every freq round. -
attack.insert_round (int) 100000 This keyword is used in the 'single' setting. The attack client can be only selected in the insert_round round. -
attack.mean (list) [0.9637] The mean value which is used in the normalization procedure of poisoning data. Notice: The length of this list must be same as the number of channels of used dataset.
attack.std (list) [0.1592] The std value which is used in the normalization procedure of poisoning data. Notice: The length of this list must be same as the number of channels of used dataset.
attack.trigger_type (str) 'edge' This keyword represents the type of used triggers Choices: {'edge', 'gridTrigger', 'hkTrigger', 'sigTrigger', 'wanetTrigger', 'fourCornerTrigger'}
attack.label_type (str) 'dirty' This keyword represents the type of used attack. It contains 'dirty'-label and 'clean'-label attacks. Now, we only support 'dirty'-label attack.
attack.edge_num (int) 100 This keyword represents the number of used good samples for edge-case attack. -
attack.poison_ratio (float) 0.5 This keyword represents the percentage of samples with pixel-wise triggers in the local dataset of attack client -
attack.scale_poisoning (bool) False This keyword represents whether to use the model scaling attack for attack client. -
attack.scale_para (float) 1.0 This keyword represents the value to amplify the model update when conducting the model scaling attack. -
attack.pgd_poisoning (bool) False This keyword represents whether to use the pgd to train the local model for attack client. -
attack.pgd_lr (float) 0.1 This keyword represents learning rate of pgd training for attack client. -
attack.pgd_eps (int) 2 This keyword represents perturbation budget of pgd training for attack client. -
attack.self_opt (bool) False This keyword represents whether to use his own training procedure for attack client. -
attack.self_lr (float) 0.05 This keyword represents learning rate of his own training procedure for attack client. -
attack.self_epoch (int) 6 This keyword represents epoch number of his own training procedure for attack client. -