config.yaml

# Parameters that describe server
n_server_rounds: 15 # The number of rounds to run FL

# Following the adaptive proximal weight setup in Appendix C3.3 of the FedProx paper: https://arxiv.org/pdf/1812.06127.pdf
# Server decides to increase or decrease the proximal weight based on the average training losses of clients that were sent during training.
# The updated proximal weight is then sent to the clients for the next round of training.
# Set the initial proximal weight to 0.0 for adaptive proximal weight setup.
adapt_proximal_weight: True # Whether to use adaptive proximal weight or not
initial_proximal_weight : 0.0 # Initial proximal weight
proximal_weight_delta : 0.1 # The amount by which to increase or decrease the proximal weight, if adapt_proximal_weight is True
proximal_weight_patience : 5 # The number of rounds to wait before increasing or decreasing the proximal weight, if adapt_proximal_weight is True

# Parameters that describe clients
n_clients: 3 # The number of clients in the FL experiment
local_epochs: 1 # The number of epochs to complete for client
batch_size: 128 # The batch size for client training