federatedcore 0.0.2

A research-oriented federal learning framework.

FederatedCore

A research-oriented federal learning framework.

Features

1. Compatibility. FederatedCore can work seamlessly with mainstream deep learning frameworks, e.g., PyTorch and Tensorflow.

2. Modular. The code of the algorithm module can be used individually.

3. Easy to use. Retrofit existing code to data parallelism with no more than 100 lines code.

Support

Attributes	Value
Framework	Pytorch, Tensorflow
Engine	parallelism, sequence
Dataset	label distribution, quality distribution
Topology	parameter server, gossip, all reduce
Communication	queue, TCP

QuickStart

Install m3u8_To_MP4 via pip

# via pypi.org
python -m pip install federatedcore

# first clone project, and install.
git clone https://github.com/songs18/FederatedCore.git
python -m pip install ./FederatedCore

A small example (FedAvg)

Implement FedAvg in fewer than 100 lines.

(/examples/FedAvg/federated_average.py)

def run(num_nodes, has_server):
    def build_host_ids():
        if has_server:
            return [i for i in range(num_nodes + 1)]
        else:
            return [i for i in range(num_nodes)]

    def build_func_libs():
        func_libs = {
            'train_dataset'   : 'self_contained_dnn',  # load_train_dataset,
            'test_dataset'    : 'self_contained_dnn',  # load_test_dataset,
            'model'           : 'self_contained_dnn',  # get_model,
            'loss'            : 'self_contained_dnn',  # get_loss,
            'optimizer'       : 'self_contained_dnn',  # get_optimizer,
            'metric_loss'     : 'self_contained_dnn',  # get_metric_loss,
            'metric_acc'      : 'self_contained_dnn',  # get_metric_acc,
            'train_step'      : 'self_contained_dnn',  # get_train_step,
            'test_step'       : 'self_contained_dnn',  # get_test_step,
            'aggregation_func': average_parameters,
        }
        return func_libs

    def build_linkers():
        node_inboxes = queuer.node_inbox(num_nodes + 1)

        linkers = list()
        for host_id in range(num_nodes):
            linker = queuer.LocalQueue(host_id, node_inboxes)
            linkers.append(linker)

        if has_server:
            linker = queuer.LocalQueue(num_nodes, node_inboxes)
            linkers.append(linker)

        return linkers

    def build_execution_plans():
        execution_plans = ExecutionPlanTemplate.client_train * 5
        execution_plans = [[[c, {}] for c in execution_plans] for _ in range(num_nodes)]

        if has_server:
            server_execution_plan = ExecutionPlanTemplate.server_init + ExecutionPlanTemplate.server_sync_train * 5
            server_execution_plan.pop(-1)
            server_execution_plan = [[s, {'iteration': 3}] for s in server_execution_plan]

            execution_plans.append(server_execution_plan)

        return execution_plans

    host_ids = build_host_ids()
    func_libs = build_func_libs()
    linkers = build_linkers()
    execution_plans = build_execution_plans()

    parallelism.run_parallel(host_ids, func_libs, linkers, execution_plans)


def main():
    num_nodes = 2
    generate_topology(num_nodes)
    split_dataset(num_nodes)
    build_host(num_nodes)
    run(num_nodes, True)


if __name__ == '__main__':
    main()