EasyFL_with_KDE/easyfl/config.yaml

# The unique identifier for each federated learning task
task_id: ""

# Provide dataset and federated learning simulation related configuration.
data:
  # The root directory where datasets are stored.
  root: "./data/"
  # The name of the dataset, support: femnist, shakespeare, cifar10, and cifar100.
  dataset: femnist
  # The data distribution of each client, support: iid, niid (for femnist and shakespeare), and dir and class (for cifar datasets).
    # `iid` means independent and identically distributed data.
    # `niid` means non-independent and identically distributed data for FEMNIST and Shakespeare.
    # `dir` means using Dirichlet process to simulate non-iid data, for CIFAR-10 and CIFAR-100 datasets.
    # `class` means partitioning the dataset by label classes, for datasets like CIFAR-10, CIFAR-100.
  split_type: "iid"

  # The minimal number of samples in each client. It is applicable for LEAF datasets and dir simulation of CIFAR-10 and CIFAR-100.
  min_size: 10
  # The fraction of data sampled for LEAF datasets. e.g., 10% means that only 10% of total dataset size are used.
  data_amount: 0.05
  # The fraction of the number of clients used when the split_type is 'iid'.
  iid_fraction: 0.1
  # Whether partition users of the dataset into train-test groups. Only applicable to femnist and shakespeare datasets.
    # True means partitioning users of the dataset into train-test groups.
    # False means partitioning each users' samples into train-test groups.
  user: False
  # The fraction of data for training; the rest are for testing.
  train_test_split: 0.9

  # The number of classes in each client. Only applicable when the split_type is 'class'.
  class_per_client: 1
  # The targeted number of clients to construct.used in non-leaf dataset, number of clients split into. for leaf dataset, only used when split type class.
  num_of_clients: 100
  # The parameter for Dirichlet distribution simulation, applicable only when split_type is `dir` for CIFAR datasets.
  alpha: 0.5

  # The targeted distribution of quantities to simulate data quantity heterogeneity.
    # The values should sum up to 1. e.g., [0.1, 0.2, 0.7].
    # The `num_of_clients` should be divisible by `len(weights)`.
    # None means clients are simulated with the same data quantity.
  weights: NULL

# The name of the model for training, support: lenet, rnn, resnet, resnet18, resnet50, vgg9.
model: lenet
# How to conduct testing, options: test_in_client or test_in_server.
  # `test_in_client` means that each client has a test set to run testing.
  # `test_in_server` means that server has a test set to run testing for the global model. Use this mode for cifar datasets.
test_mode: "test_in_client"
# The way to measure testing performance (accuracy) when test mode is `test_in_client`, support: average or weighted (means weighted average).
test_method: "average"

server:
  track: False  # Whether track server metrics using the tracking service.
  rounds: 10  # Total training round.
  clients_per_round: 5  # The number of clients to train in each round.
  test_every: 1  # The frequency of testing: conduct testing every N round.
  save_model_every: 10  # The frequency of saving model: save model every N round.
  save_model_path: ""  # The path to save model. Default path is root directory of the library.
  batch_size: 32  # The batch size of test_in_server.
  test_all: False  # Whether test all clients or only selected clients.
  random_selection: True  # Whether select clients to train randomly.
  # The strategy to aggregate client uploaded models, options: FedAvg, equal.
    # FedAvg aggregates models using weighted average, where the weights are data size of clients.
    # equal aggregates model by simple averaging.
  aggregation_stragtegy: "FedAvg"
  # The content of aggregation, options: all, parameters.
    # all means aggregating models using state_dict, including both model parameters and persistent buffers like BatchNorm stats.
    # parameters means aggregating only model parameters.
  aggregation_content: "all"

client:
  track: False  # Whether track server metrics using the tracking service.
  batch_size: 32  # The batch size of training in client.
  test_batch_size: 5  # The batch size of testing in client.
  local_epoch: 10  # The number of epochs to train in each round.
  optimizer:
    type: "Adam"  # The name of the optimizer, options: Adam, SGD.
    lr: 0.001
    momentum: 0.9
    weight_decay: 0
  seed: 0
  local_test: False  # Whether test the trained models in clients before uploading them to the server.

gpu: 0  # The total number of GPUs used in training. 0 means CPU.
distributed:  # The distributed training configurations. It is only applicable when gpu > 1.
  backend: "nccl"  # The distributed backend.
  init_method: ""
  world_size: 0
  rank: 0
  local_rank: 0

tracking:  # The configurations for logging and tracking.
  database: ""  # The path of local dataset, sqlite3.
  log_file: ""
  log_level: "INFO"  # The level of logging.
  metric_file: ""
  save_every: 1

# The configuration for system heterogeneity simulation.
resource_heterogeneous:
  simulate: False  # Whether simulate system heterogeneity in federated learning.
  # The type of heterogeneity to simulate, support iso, dir, real.
    # iso means that
  hetero_type: "real"
  level: 3  # The level of heterogeneous (0-5), 0 means no heterogeneous among clients.
  sleep_group_num: 1000  # The number of groups with different sleep time. 1 means all clients are the same.
  total_time: 1000  # The total sleep time of all clients, unit: second.
  fraction: 1  # The fraction of clients attending heterogeneous simulation.
  grouping_strategy: "greedy"  # The grouping strategy to handle system heterogeneity, support: random, greedy, slowest.
  initial_default_time: 5  # The estimated default training time for each training round, unit: second.
  default_time_momentum: 0.2  # The default momentum for default time update.

seed: 0  # The random seed.