data_hetero_femnist_lenet5.yml

clients:
    # Type
    type: simple

    # The total number of clients
    total_clients: 100

    # The number of clients selected in each round
    per_round: 5

    # Should the clients compute test accuracy locally?
    do_test: false

    # Whether simulate clients or not
    simulation: true

server:
    address: 127.0.0.1
    port: 8000

    disable_clients: false

    synchronous: false

    periodic_interval: 3

data:
    # The training and testing dataset
    datasource: MNIST

    # Where the dataset is located
    data_path: ./data

    # Should concurrent client-side dataset download be allowed?
    concurrent_download: true

    # Number of samples in each partition
    partition_size: 500

    # IID or non-IID?
    sampler: all_inclusive

    # The random seed for sampling data
    random_seed: 4

trainer:
    # The type of the trainer
    type: basic

    # The maximum number of training rounds
    rounds: 5

    # Whether the training should use multiple GPUs if available
    parallelized: true

    # The maximum number of clients running concurrently
    max_concurrency: 1

    # The target accuracy
    target_accuracy: 0.94

    # Number of epoches for local training in each communication round
    epochs: 5
    batch_size: 32
    optimizer: SGD
    learning_rate: 0.01
    momentum: 0.9
    weight_decay: 0.0

    # The machine learning model
    model_name: lenet5

    num_classes: 62

algorithm:
    # Aggregation algorithm
    type: fedavg

results:
    results_dir: ./
    types: accuracy, training_time, round_time
    plot: training_time&accuracy