Server

type

The type of the server.

• fedavg a Federated Averaging (FedAvg) server.
• fedavg_cross_silo a Federated Averaging server that handles cross-silo federated learning by interacting with edge servers rather than with clients directly. When this server is used, algorithm.type must be fedavg.
• fedavg_gan a Federated Averaging server that handles Generative Adversarial Networks (GANs).
• fedavg_he a Federated Averaging server that handles model updates after homomorphic encryption. When this server is used, the clients need to enable inbound processor model_decrypt to decrypt the global model from server, and outbound processor model_encrypt to encrypt the model updates.
• fedavg_personalized a Federated Averaging server that supports all-purpose personalized federated learning by controlling when and which group of clients are to perform local personalization.
• fedavg_mpc_additive a Federated Averaging server that reconstructs additive MPC shares before aggregation. Requires clients of type mpc with the mpc_model_encrypt_additive processor.
• fedavg_mpc_shamir a Federated Averaging server that reconstructs Shamir MPC shares before aggregation. Requires clients of type mpc with the mpc_model_encrypt_shamir processor.
• split_learning a Split Learning server that supports training different kinds of models in split learning framework. When this server is used, the clients.per_round in the configuration should be set to 1. Users should define the rules for updating models weights before cut from the clients to the server in the callback function on_update_weights_before_cut, depending on the specific model they use.
• fedavg_personalized a personalized federated learning server that starts from a number of regular rounds of federated learning. In these regular rounds, only a subset of the total clients can be selected to perform the local update (the ratio of which is a configuration setting). After all regular rounds are completed, it starts a final round of personalization, where a selected subset of clients perform local training using their local dataset.

address

The address of the central server, such as 127.0.0.1.

port

The port number of the central server, such as 8000.

disable_clients

If this optional setting is true, the server will not launched client processes on the same physical machine. This is useful when the server is deployed in the cloud and connected to by remote clients.

s3_endpoint_url

The endpoint URL for an S3-compatible storage service, used for transferring payloads between clients and servers.

s3_bucket

The bucket name for an S3-compatible storage service, used for transferring payloads between clients and servers.

zk_address

The address of the ZooKeeper service used to coordinate MPC clients when S3 storage is enabled.

zk_port

The port of the ZooKeeper service used to coordinate MPC clients when S3 storage is enabled.

random_seed

The random seed used for selecting clients (and sampling the test dataset on the server, if needed) so that experiments are reproducible.

ping_interval

The time interval in seconds at which the server pings the client.

Default value: 3600

ping_timeout

The time in seconds that the client waits for the server to respond before disconnecting.

Default value: 3600

synchronous

Whether training session should operate in synchronous (true) or asynchronous (false) mode.

periodic_interval

The time interval for a server operating in asynchronous mode to aggregate received updates. Any positive integer could be used for periodic_interval.

Default value: 5 seconds

Note

This is only used when we are not simulating the wall-clock time using the simulate_wall_time setting below.

simulate_wall_time

Whether or not the wall clock time on the server is simulated. This is useful when clients train in batches, rather than concurrently, due to limited resources (such as a limited amount of CUDA memory on the GPUs).

staleness_bound

In asynchronous mode, whether or not we should wait for clients who are behind the current round (stale) by more than this value. Any positive integer could be used for staleness_bound.

Default value: 0

minimum_clients_aggregated

When operating in asynchronous mode, the minimum number of clients that need to arrive before aggregation and processing by the server. Any positive integer could be used for minimum_clients_aggregated.

Default value: 1

minimum_edges_aggregated

When operating in asynchronous cross-silo federated learning, the minimum number of edge servers that need to arrive before aggregation and processing by the central server. Any positive integer could be used for minimum_edges_aggregated.

Default value: algorithm.total_silos

do_test

Whether the server tests the global model and computes the global accuracy or perplexity.

Default value: true

model_path

The path to the pretrained and trained models.

Default value: <base_path>/models/pretrained, where <base_path> is specified in the general section.

checkpoint_path

The path to temporary checkpoints used for resuming the training session.

Default value: <base_path>/checkpoints, where <base_path> is specified in the general section.

mpc_data_path

Directory where MPC coordination artefacts (e.g., round_info) and optional debug payloads are stored when running locally. Defaults to <base_path>/mpc_data.

outbound_processors

A list of processors to apply on the payload before sending it out to the clients. Multiple processors are permitted.

• unstructured_pruning Process unstructured pruning on model weights. The model_compress processor needs to be applied after it in the configuration file or the communication overhead will not be reduced.
• structured_pruning Process structured pruning on model weights. The model_compress processor needs to be applied after it in the configuration file or the communication overhead will not be reduced.
• model_compress Compress model parameters with Zstandard compression algorithm. Must be placed as the last processor if applied.

inbound_processors

A list of processors to apply on the payload right after receiving. Multiple processors are permitted.

• model_decompress Decompress model parameters. Must be placed as the first processor if model_compress is applied on the client side.

outbound_feature_ndarrays. - model_dequantize Dequantize model parameters back to the 32-bit floating number format. - model_dequantize_qsgd Dequantize model parameters quantized with QSGD.

mpc_shamir_threshold

Overrides the number of shares required to reconstruct Shamir-secret-shared tensors. Defaults to number_of_selected_clients - 2.

downlink_bandwidth

The server's estimated downlink capacity (server to clients or central server to edge servers in cross-silo training) in Mbps, used for computing the transmission time (see compute_comm_time in the clients section).

Default value: 100

uplink_bandwidth

The server's estimated uplink capacity (server to clients or central server to edge servers in cross-silo training) in Mbps, used for computing the transmission time (see compute_comm_time in the clients section).

Default value: 100

edge_downlink_bandwidth

The edge server's estimated downlink capacity (an edge server to its clients) in Mbps, used for computing the transmission time (see compute_comm_time in the clients section).

Default value: same as downlink_bandwidth

edge_uplink_bandwidth

The edge server's estimated uplink capacity (an edge server to its clients) in Mbps, used for computing the transmission time (see compute_comm_time in the clients section).

Default value: same as uplink_bandwidth

do_personalization_interval

The round interval for a server commanding when to perform personalization.

Default value: 0, meaning that no personalization will be performed.

do_personalization_group

The group of clients that is required by the server to perform personalization. There are three options, including "total", "participant", and "nonparticipant".

Default value: participant, meaning the clients participating in training will be used to perform personalization.