Problem with the function " _local_test_on_all_clients" in "https://github.com/FedML-AI/FedML/blob/master/python/fedml/simulation/sp/fedavg/fedavg_api.py"

def _local_test_on_all_clients(self, round_idx):

logging.info("################local_test_on_all_clients : {}".format(round_idx))

train_metrics = {"num_samples": [], "num_correct": [], "losses": []}

test_metrics = {"num_samples": [], "num_correct": [], "losses": []}

**client = self.client_list[0]**

for client_idx in range(self.args.client_num_in_total):
"""
Note: for datasets like "fed_CIFAR100" and "fed_shakespheare",
the training client number is larger than the testing client number
"""
if self.test_data_local_dict[client_idx] is None:
continue
client.update_local_dataset(
0,
self.train_data_local_dict[client_idx],
self.test_data_local_dict[client_idx],
self.train_data_local_num_dict[client_idx],
)
# train data
train_local_metrics = client.local_test(False)
train_metrics["num_samples"].append(copy.deepcopy(train_local_metrics["test_total"]))
train_metrics["num_correct"].append(copy.deepcopy(train_local_metrics["test_correct"]))
train_metrics["losses"].append(copy.deepcopy(train_local_metrics["test_loss"]))

# test data
test_local_metrics = client.local_test(True)
test_metrics["num_samples"].append(copy.deepcopy(test_local_metrics["test_total"]))
test_metrics["num_correct"].append(copy.deepcopy(test_local_metrics["test_correct"]))
test_metrics["losses"].append(copy.deepcopy(test_local_metrics["test_loss"]))

# test on training dataset
train_acc = sum(train_metrics["num_correct"]) / sum(train_metrics["num_samples"])
train_loss = sum(train_metrics["losses"]) / sum(train_metrics["num_samples"])

# test on test dataset
test_acc = sum(test_metrics["num_correct"]) / sum(test_metrics["num_samples"])
test_loss = sum(test_metrics["losses"]) / sum(test_metrics["num_samples"])

stats = {"training_acc": train_acc, "training_loss": train_loss}
if self.args.enable_wandb:
wandb.log({"Train/Acc": train_acc, "round": round_idx})
wandb.log({"Train/Loss": train_loss, "round": round_idx})

mlops.log({"Train/Acc": train_acc, "round": round_idx})
mlops.log({"Train/Loss": train_loss, "round": round_idx})
logging.info(stats)

stats = {"test_acc": test_acc, "test_loss": test_loss}
if self.args.enable_wandb:
wandb.log({"Test/Acc": test_acc, "round": round_idx})
wandb.log({"Test/Loss": test_loss, "round": round_idx})

mlops.log({"Test/Acc": test_acc, "round": round_idx})
mlops.log({"Test/Loss": test_loss, "round": round_idx})
logging.info(stats)

In the 4th line of the function, why is always the zeroth client selected? This way, the testing happens on the model corresponding to the zeroth client only, but we want the average test error on the local dataset for each client, isn't it?