# It seems that fit() is not called normally **URL:** \ **Category:** Flower Help - Beginners\ **Tags:** flower\ **Created:** [July 10, 2025, 7:47am UTC](https://discuss.flower.ai/t/it-seems-that-fit-is-not-called-normally/1039 "2025-07-10T07:47:06Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![josephkung](https://avatars.discourse-cdn.com/v4/letter/j/74df32/32.png) [@josephkung](https://discuss.flower.ai/u/josephkung)\ **Post date:** [July 10, 2025, 7:47am UTC](https://discuss.flower.ai/t/it-seems-that-fit-is-not-called-normally/1039/1 "2025-07-10T07:47:06Z")
This is the log of my server.py, all of which are 0 results and 2 failures INFO : INFO : [ROUND 1] INFO : configure\_fit: strategy sampled 2 clients (out of 2) INFO : aggregate\_fit: received 0 results and 2 failures [Server] Round 1: 0 results, 2 failures Round 1 evaluation loss: 2.3127, accuracy: 0.0989 INFO : fit progress: (1, 2.3127286434173584, {‘accuracy’: 0.09888888895511627}, 9.558189600007609) INFO : configure\_evaluate: strategy sampled 2 clients (out of 2) INFO : aggregate\_evaluate: received 0 results and 2 failures INFO : INFO : [ROUND 2] INFO : configure\_fit: strategy sampled 2 clients (out of 2) INFO : aggregate\_fit: received 0 results and 2 failures [Server] Round 2: 0 results, 2 failures Round 2 evaluation loss: 2.3127, accuracy: 0.0989 INFO : fit progress: (2, 2.3127286434173584, {‘accuracy’: 0.09888888895511627}, 9.695105200007674) INFO : configure\_evaluate: strategy sampled 2 clients (out of 2) INFO : aggregate\_evaluate: received 0 results and 2 failures INFO : INFO : [ROUND 3] INFO : configure\_fit: strategy sampled 2 clients (out of 2) INFO : aggregate\_fit: received 0 results and 2 failures [Server] Round 3: 0 results, 2 failures Round 3 evaluation loss: 2.3127, accuracy: 0.0989 INFO : fit progress: (3, 2.3127286434173584, {‘accuracy’: 0.09888888895511627}, 9.825406700008898) INFO : configure\_evaluate: strategy sampled 2 clients (out of 2) INFO : aggregate\_evaluate: received 0 results and 2 failures … I don’t know where the problem is… This is my server.py import flwr as fl import pandas as pd import numpy as np from model import dnn\_model from data\_process import split\_data from flwr.common import parameters\_to\_ndarrays, FitIns, EvaluateIns #split\_data() server\_mean = np.load(“server\_data/mean.npy”) server\_std = np.load(“server\_data/std.npy”) val\_df = pd.read\_csv(“server\_data/val.csv”) y\_val = val\_df[“Label”].values x\_val = val\_df.drop(columns=[“Label”]).values.astype(np.float32) x\_val = (x\_val - server\_mean) / (server\_std + 1e-6) model = dnn\_model() class SaveClientsData(fl.server.strategy.FedAvg): def **init** (self, model, val\_data, Global\_mean, Global\_std, \*\*kwargs): super(). **init** (\*\*kwargs) self.model = model self.x\_val, self.y\_val = val\_data self.global\_mean = Global\_mean self.global\_std = Global\_std ``` def configure_fit(self, server_round, parameters, client_manager): config = { "global_mean": self.global_mean.tolist(), "global_std": self.global_std.tolist(), } allclients = client_manager.sample( num_clients=self.min_fit_clients, min_num_clients=self.min_fit_clients ) return [(client, FitIns(parameters, config)) for client in allclients] def configure_evaluate(self, server_round, parameters, client_manager): cfg = { "global_mean": self.global_mean.tolist(), "global_std": self.global_std.tolist(), } allclients = client_manager.sample( num_clients=self.min_fit_clients, min_num_clients=self.min_fit_clients ) return [(client, EvaluateIns(parameters, cfg)) for client in allclients] def aggregate_fit(self, rnd, result, failure): print(f"[Server] Round {rnd}: {len(result)} results, {len(failure)} failures") if not result: return None, {} acc = [] for _, fit_res in result: metrics = fit_res.metrics if metrics and "accuracy" in metrics: acc.append(metrics["accuracy"]) if acc: print(f"Round {rnd} accuracies: {acc}") return super().aggregate_fit(rnd, result, failure) def evaluate(self, server_rnd, parameters, config=None): weights = parameters_to_ndarrays(parameters) self.model.set_weights(weights) loss, accuracy = self.model.evaluate(x_val, y_val, verbose=0) print(f"Round {server_rnd} evaluation loss: {loss:.4f}, accuracy: {accuracy:.4f}") return loss, {"accuracy": accuracy} ``` print(“[Server] Waiting for clients to connect…”) fl.server.start\_server( server\_address=“0.0.0.0:8080”, config=fl.server.ServerConfig(num\_rounds=10, round\_timeout=60), strategy=SaveClientsData( model = model, val\_data=(x\_val, y\_val), Global\_mean=server\_mean, Global\_std=server\_std, min\_fit\_clients = 2, min\_evaluate\_clients = 2, min\_available\_clients = 2, fraction\_fit=1.0 ) ) And this is my client.py: import flwr as fl import pandas as pd import numpy as np from model import dnn\_model import argparse class FlClient(fl.client.NumPyClient): def **init** (self , client\_num): self.model = dnn\_model() self.client\_num = client\_num self.load\_data() self.is\_standardized = False ``` print(f"[Client {self.client_num}] Samples: {self.x_train.shape}, {self.y_train.shape}") def load_data(self): df = pd.read_csv(f'client_data/client_{self.client_num}.csv') y = df["Label"].values x = df.drop(columns=["Label", "Split"]).values.astype(np.float32) is_split = df["Split"].values self.x_train = x[is_split == "train"] self.y_train = y[is_split == "train"] self.x_test = x[is_split == "test"] self.y_test = y[is_split == "test"] def standardize(self, server_mean, server_std): self.x_train = (self.x_train - server_mean) / server_std self.x_test = (self.x_test - server_mean) / server_std if np.any(np.isnan(self.x_train)) or np.any(np.isinf(self.x_train)): print(f"[Client {self.client_num}] Data has NaN or inf after standardization") self.is_standardized = True def get_parameters(self, config): return self.model.get_weights() def __del__ (self): print(f"[Client {self.client_num}] shutting down") def fit(self, parameters, config): try: print(f"[Client {self.client_num}] fit() triggered.") print(f"Received parameters: {[w.shape for w in parameters]}") print(f"Model expects: {[w.shape for w in self.model.get_weights()]}") if not self.is_standardized: if "global_mean" in config and "global_std" in config: server_mean = np.array(config["global_mean"]) server_std = np.array(config["global_std"]) self.standardize(server_mean, server_std) else: print(f"[Client {self.client_num}] Missing standardization config.") self.model.set_weights(parameters) history = self.model.fit(self.x_train, self.y_train, epochs=10, batch_size=32, verbose=0) acc = history.history["accuracy"][-1] return self.model.get_weights(), len(self.x_train), {"accuracy": acc} except Exception as e: import traceback traceback.print_exc() raise e def evaluate(self, parameters, config): if not self.is_standardized: if "global_mean" in config and "global_std" in config: server_mean = np.array(config["global_mean"]) server_std = np.array(config["global_std"]) self.standardize(server_mean, server_std) else: print(f"[Client {self.client_num}] Missing standardization config. Skipping standardization.") self.model.set_weights(parameters) loss, acc = self.model.evaluate(self.x_test, self.y_test, verbose=0) return loss, len(self.x_test), {"accuracy": acc} ``` def main(): parser = argparse.ArgumentParser() parser.add\_argument(“–client\_num”, type=int, required=True) args = parser.parse\_args() ``` fl.client.start_numpy_client( server_address="127.0.0.1:8080", client=FlClient(args.client_num) ) ``` if **name** == “ **main** ”: main() ---
**Author:** ![daniel](https://dub1.discourse-cdn.com/flex013/user_avatar/discuss.flower.ai/daniel/32/77_2.png) [@daniel](https://discuss.flower.ai/u/daniel)\ **Post date:** [July 13, 2025, 5:15pm UTC](https://discuss.flower.ai/t/it-seems-that-fit-is-not-called-normally/1039/2 "2025-07-13T17:15:58Z")
Hi @josephkung, welcome to the Flower Community! Having `0 results and 2 failures` indicates that something on the client side might be wrong. The code also uses `start_numpy_client`, which is deprecated. My recommendation would be to create a new project using `flwr new`. This project can be directly started using `flwr run`. Once that’s confirmed to work (the built-in templates are well tested and work out of the box), you can start to move the code from the project above into this new project step-by-step. ---
**Author:** ![josephkung](https://avatars.discourse-cdn.com/v4/letter/j/74df32/32.png) [@josephkung](https://discuss.flower.ai/u/josephkung)\ **Post date:** [July 13, 2025, 5:52pm UTC](https://discuss.flower.ai/t/it-seems-that-fit-is-not-called-normally/1039/3 "2025-07-13T17:52:11Z")
Hello @daniel ! First of all, thank you very much for your reply. In your suggestion (using flwr new), does it mean that my flower version is too low? I use vscode for programming, and the version of flower is 1.19.0. How should I implement your suggestion? Do you mean to use pip install -U flwr to upgrade the version and then try again? Or if there are relevant practical articles for me to refer to, I am also very willing to learn. Thank you! ---
**Author:** ![daniel](https://dub1.discourse-cdn.com/flex013/user_avatar/discuss.flower.ai/daniel/32/77_2.png) [@daniel](https://discuss.flower.ai/u/daniel)\ **Post date:** [July 13, 2025, 6:08pm UTC](https://discuss.flower.ai/t/it-seems-that-fit-is-not-called-normally/1039/4 "2025-07-13T18:08:50Z")
Flower 1.19 is the latest version. Once installed, you can use the `flwr` CLI to create and run projects using a number of templates (via `flwr new` and `flwr run`). Here’s a tutorial that covers it: [Quickstart PyTorch - Flower Framework](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html) ---
**Author:** ![josephkung](https://avatars.discourse-cdn.com/v4/letter/j/74df32/32.png) [@josephkung](https://discuss.flower.ai/u/josephkung)\ **Post date:** [July 13, 2025, 6:17pm UTC](https://discuss.flower.ai/t/it-seems-that-fit-is-not-called-normally/1039/5 "2025-07-13T18:17:34Z")
Thank you very much for your quick reply and the article! Since it’s getting late, I will study it carefully tomorrow. I hope I will have this opportunity to ask you for advice when I encounter difficulties.