Examples

We provide examples in examples folder, including examples to quickly run experiments with FEMNIST, CIFAR-10, customize tracking, and more.

High-level Introduction

COALA provides numerous existing models and datasets. Models include LeNet, RNN, VGG9, and ResNet. Datasets include Femnist, Shakespeare, CIFAR-10, and CIFAR-100. This note will present how to start training with these existing models and standard datasets.

COALA provides three types of high-level APIs: registration, initialization, and execution. Registration is for registering customized components, which we will introduce in the following notes. In this note, we focus on initialization and execution.

Simplest Run

We can run federated learning with only two lines of code (not counting the import statement). It executes training with default configurations: simulating 100 clients with the FEMNIST dataset and randomly selecting 5 clients for training in each training round. We explain more about the configurations in another note.

Note: we package default partitioning of Femnist data to avoid downloading the whole dataset.

import coala

# Initialize federated learning with default configurations.
coala.init()
# Execute federated learning training.
coala.run()

Run with Configurations

You can specify configurations to overwrite the default configurations.

import coala

# Customized configuration.
config = {
    "data": {"dataset": "cifar10", "split_type": "class", "num_of_clients": 100},
    "server": {"rounds": 5, "clients_per_round": 2},
    "client": {"local_epoch": 5},
    "model": "resnet18",
    "test_mode": "test_in_server",
}
# Initialize federated learning with default configurations.
coala.init(config)
# Execute federated learning training.
coala.run()

In the example above, we run training with model ResNet-18 and CIFAR-10 dataset that is partitioned into 100 clients by label class. It runs training with 2 clients per round for 5 rounds. In each round, each client trains 5 epochs.