def aggregate_metrics_across_clients(metrics):
global metrics_name
output = collections.OrderedDict()
for metric in metrics_name:
if metric == 'num_examples':
output[metric] = tff.federated_sum(getattr(metrics, metric))
output['per_client/' + metric] = tff.federated_collect(
getattr(metrics, metric))
else:
output[metric] = tff.federated_mean(getattr(metrics, metric),
metrics.num_examples)
output['per_client/' + metric] = tff.federated_collect(
getattr(metrics, metric))
return output
def evaluate(datasets, client_states):
outputs = tff.federated_map(evaluate_client_tf, (datasets, client_states))
confusion_matrix = tff.federated_sum(outputs.confusion_matrix)
aggregated_metrics = model.federated_output_computation(outputs.metrics)
collected_metrics = tff.federated_collect(outputs.metrics)
return confusion_matrix, aggregated_metrics, collected_metrics
def comp(client_data):
return tff.federated_collect(client_data)