Algorithm

type

Aggregation algorithm.

The input should be:

• fedavg the federated averaging algorithm
• split_learning the Split Learning algorithm
• fedavg_personalized the personalized federated learning algorithm
• pfedgraph the Personalized Federated Learning with Inferred Collaboration Graphs algorithm

cross_silo

Whether or not cross-silo training should be used.

total_silos

The total number of silos (edge servers). The input could be any positive integer.

local_rounds

The number of local aggregation rounds on edge servers before sending aggregated weights to the central server. The input could be any positive integer.

fedavg_personalized

Whether or not the personalized training should be used.

local_layer_names

Local layers in a model should remain local at the clients during personalized FL training, and should not be aggregated at the server.

participating_clients_ratio

A float to show the proportion of clients participating in the federated training process. It is under personalization, which is a sub-config path that contains other personalized training parameters.

Default value: 1.0

pfedgraph

Configuration for pFedGraph.

pfedgraph_alpha

Hyper-parameter controlling the collaboration graph update.

Default value: 0.8

pfedgraph_lambda

Regularization strength for cosine similarity in the local objective.

Default value: 0.01

pfedgraph_similarity_metric

Similarity metric scope for graph inference. Use all for all parameters or fc to focus on the final fully-connected layers.

Default value: all

pfedgraph_similarity_layers

Optional list of layer name substrings to use when computing model similarity for graph inference. Overrides pfedgraph_similarity_metric when provided.