mindspore_federated.data_join

class mindspore_federated.FLDataWorker(config: dict)[source]

Unlike horizontal federated learning, two participants (leader and follower) have the same sample space for training or inference in vertical federated learning. Therefore, the data intersection must be done collaboratively before both parties in vertical federated learning initiate training or inference. Both parties must read their respective original data and extract the ID (unique identifier of each data, and none of them is the same) corresponding to each data for intersection (i.e., finding the intersection). Then, both parties obtain features or tags from the original data based on the intersected IDs. Finally, each side exports the persistence file and reads the data in the reordering manner before subsequent training or inference. Data join worker is used to export data.

Parameters

config (dict) –

the key/value of dict defined as below

  • role(str): Role of the worker, which must be set in both leader and follower. Supports "leader", "follower". Default: "leader".

  • bucket_num(int): The number of buckets. If leader has set a valid value, the value set by follower will not be used. Default: 5.

  • store_type(str): The origin data store type. Now only support csv/mysql. Supports "csv", "mysql". Default: "csv".

  • data_schema_path(str): Path of data schema file, which must be set in both leader and follower. User need to provide the column name and type of the data to be exported in the schema. The schema needs to be parsed as a two-level key-value dictionary. The key of the first-level dictionary is the column name, and the value is the second-level dictionary. The key of the second-level dictionary must be a string: type, and the value is the type of the exported data. Currently, the types support [int32, int64, float32, float64, string, bytes]. Default: "vfl/leader_schemal.yaml".

  • primary_key(str): The primary key. If leader has set a valid value, the value set by follower will not be used. Default: "oaid".

  • main_table_files(Union(list(str), str)): The raw data paths, which must be set in both leader and follower. Default: "vfl/input/leader/".

  • mysql_host(str): Host where the database server is located. Default: "127.0.0.1".

  • mysql_port(int): MySQL port to use, usually use 3306. Default: 3306.

  • mysql_database(str): Database to use, None to not use a particular one. Default: "".

  • mysql_charset(str): Charset you want to use. Default: "".

  • mysql_user(str): Username to login mysql. Default: "test".

  • mysql_password(str): Password to login mysql. Default: "1234".

  • mysql_table_name(str): The table that contains origin data. Default: "test_data".

  • server_name(str): Local http server name, used for communication. Default: "leader_node".

  • http_server_address(str): Local IP and Port Address, which must be set in both leader and follower. Default: "127.0.0.1:1086".

  • remote_server_name(str): Remote http server name, used for communication. Default: "follower_node".

  • remote_server_address(str): Peer IP and Port Address, which must be set in both leader and follower. Default: "127.0.0.1:1087".

  • enable_ssl(bool): SSL mode enabled or disabled for communication. Supports [True, False]. Default: False.

  • server_password(str): The server password to decode the p12 file. For security please giving it in start command line. Default: "123456".

  • client_password(str): The client password to decode the p12 file. For security please giving it in start command line. Default: "123456".

  • server_cert_path(str): Certificate file path for server. Default: "server.p12".

  • client_cert_path(str): Certificate file path for client. Default: "client.p12".

  • ca_cert_path(str): CA server certificate file path. Default: “cacert.pem".

  • crl_path(str): CRL certificate file path. Default: "".

  • cipher_list(str): Encryption suite supported by ssl. Default: "ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA- AES128-GCM-SHA256:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-CHACHA20-POLY1305: ECDHE-PSK-CHACHA20-POLY1305:ECDHE-ECDSA-AES128-CCM:ECDHE-ECDSA-AES256-CCM: ECDHE-ECDSA-CHACHA20-POLY1305".

  • cert_expire_warning_time_in_day(str): Warning time before the certificate expires. Default: 90.

  • join_type(str): The data join type. If leader has set a valid value, the value set by follower will not be used. Now only support "psi". Default: "psi".

  • thread_num(int): The thread number of psi. Default: 0.

  • output_dir(str): The output directory, which must be set in both leader and follower. Default: "vfl/output/leader/".

  • shard_num(int): The output number of each bucket when export. If leader has set a valid value, the value set by follower will not be used. Default: 1.

More details refer to vfl_data_join_config.

Examples

>>> from mindspore_federated import FLDataWorker
>>> from mindspore_federated.common.config import get_config
>>>
>>> current_dir = os.path.dirname(os.path.abspath(__file__))
>>> args = get_config(os.path.join(current_dir, "vfl/vfl_data_join_config.yaml"))
>>> dict_cfg = args.__dict__
>>>
>>> worker = FLDataWorker(config=dict_cfg)
>>> worker.do_worker()
communicator()[source]

If you want to use same communicator in both data join and vfl model training, you can call this function to obtain the communicator instance.

do_worker()[source]

Execute data join worker according to the config.

Examples

>>> cfg_dict = dict()
>>> cfg_dict['server_name'] = "james"
>>> cfg_dict['http_server_address'] = "127.0.0.1:1984"
>>> cfg_dict['remote_server_name'] = "kobe"
>>> cfg_dict['remote_server_address'] = "127.0.0.1:1230"
>>> cfg_dict['enable_ssl'] = True
>>> cfg_dict['main_table_files'] = "leader_data.csv"
>>> cfg_dict['role'] = "leader"
>>> cfg_dict['output_dir'] = "leader/"
>>> cfg_dict['data_schema_path'] = "leader_schema.yaml"
>>> cfg_dict['shard_num'] = 2
>>> work = FLDataWorker(cfg_dict)
>>> work.do_worker()

data join

mindspore_federated.data_join.load_mindrecord(input_dir, seed=0, **kwargs)[source]

Load MindRecord files.

Parameters

  • input_dir (str) – Input directory for storing MindRecord-related files.

  • seed (int) – The random seed. Default: 0.

Returns

MindDataset, Order-preserving datasets.

Note

This API transparently transfers the kwargs to MindDataset. For details about more hyper parameters in kwargs, refer to mindspore.dataset.MindDataset .

Examples

>>> dataset = load_mindrecord(input_dir="input_dir", seed=0, shuffle=True)
>>> for batch in dataset.create_tuple_iterator():
...     print(batch)
mindspore_federated.common.config.get_config(cfg_file)[source]

Parse yaml file to get configuration information.

Parameters

cfg_file (str) – the directory of yaml file.

Returns

argparse, the configuration information parsed from yaml file.

Note

Using this function get configuration information to construct FLDataWorker.

Examples

>>> current_dir = os.path.dirname(os.path.abspath(__file__))
>>> args = get_config(os.path.join(current_dir, "vfl/vfl_data_join_config.yaml"))
>>> dict_cfg = args.__dict__
>>>
>>> worker = FLDataWorker(config=dict_cfg)
...