def _fit(client: Client, fit_msg: ServerMessage.FitIns) -> ClientMessage:
# Deserialize fit instruction
fit_ins = serde.fit_ins_from_proto(fit_msg)
# Perform fit
fit_res = client.fit(fit_ins)
# Serialize fit result
fit_res_proto = serde.fit_res_to_proto(fit_res)
return ClientMessage(fit_res=fit_res_proto)
def _evaluate(client: Client, evaluate_msg: ServerMessage.EvaluateIns) -> ClientMessage:
# Deserialize evaluate instruction
evaluate_ins = serde.evaluate_ins_from_proto(evaluate_msg)
# Perform evaluation
evaluate_res = client.evaluate(evaluate_ins)
# Serialize evaluate result
evaluate_res_proto = serde.evaluate_res_to_proto(evaluate_res)
return ClientMessage(evaluate_res=evaluate_res_proto)
def _get_properties(
client: Client, properties_msg: ServerMessage.PropertiesIns
) -> ClientMessage:
# Deserialize get_properties instruction
properties_ins = serde.properties_ins_from_proto(properties_msg)
# Request for properties
properties_res = client.get_properties(properties_ins)
# Serialize response
properties_res_proto = serde.properties_res_to_proto(properties_res)
return ClientMessage(properties_res=properties_res_proto)
def _federated_personalized_evaluate(
client: Client, fpe_msg: ServerMessage.FederatedPersonalizedEvaluateIns
) -> ClientMessage:
# Deserialize fpe instruction
fpe_ins = serde.fpe_ins_from_proto(fpe_msg)
# Perform federated personalized evaluation
fpe_res = client.federated_personalized_evaluate(fpe_ins)
# Serialize fpe result
fpe_res_proto = serde.fpe_res_to_proto(fpe_res)
return ClientMessage(fpe_res=fpe_res_proto)
def _get_parameters(client: Client) -> ClientMessage:
# No need to deserialize get_parameters_msg (it's empty)
parameters_res = client.get_parameters()
parameters_res_proto = serde.parameters_res_to_proto(parameters_res)
return ClientMessage(parameters_res=parameters_res_proto)