Integrate zero-knowledge proof check for horizontal FL

.gitignore

@@ -154,4 +154,5 @@ data/dataset/breast_cancer_data/client0/log_reg/
 data/dataset/breast_cancer_data/client1/log_reg/
 data/dataset/breast_cancer_data/client2/log_reg/
 
-python/data/
+src/executor/python/data/bank/
+src/executor/python/data/mnist/

src/executor/python/hfl/README.md

@@ -21,23 +21,24 @@ Download the bank dataset and split it into 3 partitions.
 # 3. run the following command which:
 #    (1) splits the dataset into N subsets
 #    (2) splits each subsets into train set and test set (8:2)
-python -m bank N
+#    (3) puts the split sub-datasets to /FALCON_PATH/src/executor/python/data/bank/
+python3 -m bank N
 ```
 
 ## Run the example
 
 Run the server first (set the number of epochs to 3)
 
 ```bash
-python -m src.server -m 3 --num_clients 3
+python3 -m src.server_zkp -m 3 --num_clients 3
 ```
 
 Then, start 3 clients in different terminal
 
 ```bash
-python -m src.client --model mlp --data bank -m 3 -i 0 -d non-iid
-python -m src.client --model mlp --data bank -m 3 -i 1 -d non-iid
-python -m src.client --model mlp --data bank -m 3 -i 2 -d non-iid
+python3 -m src.client_zkp --model mlp --data bank --data_dir /opt/falcon/src/executor/python/data/bank -m 3 --num_clients 3 -i 0
+python3 -m src.client_zkp --model mlp --data bank --data_dir /opt/falcon/src/executor/python/data/bank -m 3 --num_clients 3 -i 1
+python3 -m src.client_zkp --model mlp --data bank --data_dir /opt/falcon/src/executor/python/data/bank -m 3 --num_clients 3 -i 2
 ```
 
 Finally, the server and clients finish the FL training. 

src/executor/python/hfl/src/client.py

@@ -3,7 +3,8 @@
 import socket
 
 from .proto import interface_pb2 as proto
-from .utils import parseargs, get_dataset
+from .utils import parseargs
+from .dataset.data_loader import get_dataset
 
 import numpy as np
 
@@ -95,50 +96,6 @@ def push(self) -> None:
         utils.send_message(self.sock, message)
 
 
-# Calculate accuracy
-def accuracy(pred, target):
-    # y is network output to be compared with ground truth (int)
-    y = np.argmax(pred, axis=1)
-    a = y == target
-    correct = np.array(a, "int").sum()
-    return correct
-
-
-# Data partition according to the rank
-def partition(global_rank, world_size, train_x, train_y, val_x, val_y):
-    # Partition training data
-    data_per_rank = train_x.shape[0] // world_size
-    idx_start = global_rank * data_per_rank
-    idx_end = (global_rank + 1) * data_per_rank
-    train_x = train_x[idx_start:idx_end]
-    train_y = train_y[idx_start:idx_end]
-
-    # Partition evaluation data
-    data_per_rank = val_x.shape[0] // world_size
-    idx_start = global_rank * data_per_rank
-    idx_end = (global_rank + 1) * data_per_rank
-    val_x = val_x[idx_start:idx_end]
-    val_y = val_y[idx_start:idx_end]
-    return train_x, train_y, val_x, val_y
-
-
-
-def get_data(data, data_dist="iid", device_id=None):
-    if data == "bank":
-        train_x, train_y, val_x, val_y, num_classes = bank.load(device_id)
-    else:
-        raise NotImplementedError
-    return train_x, train_y, val_x, val_y, num_classes
-
-
-def get_model(model, num_channels=None, num_classes=None, data_size=None):
-    if model == "mlp":
-        model = MLP_Bank(dim_in=data_size, dim_hidden=64, dim_out=num_classes)
-    else:
-        raise NotImplementedError
-    return model
-
-
 def run(
         args,
         global_rank,

src/executor/python/hfl/src/client_zkp.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+
+import socket
+import sys
+
+from .proto import interface_pb2 as proto
+from .utils import parseargs, check_defense_type, send_string, receive_string, flatten_model_weights, flattened_weight_size
+from .dataset.data_loader import get_dataset
+
+import numpy as np
+import copy
+import os
+import base64
+from tqdm import tqdm
+
+from . import utils
+from .dataset import bank
+from .models import MLP_Bank, CNNMnist
+
+from .update import LocalUpdate, test_inference
+
+import risefl_interface
+
+
+class Client:
+    """Client sends and receives protobuf messages.
+
+    Create and start the server, then use pull and push to communicate with the server.
+
+    Attributes:
+        global_rank (int): The rank in training process.
+        host (str): Host address of the server.
+        port (str): Port of the server.
+        sock (socket.socket): Socket of the client.
+        weights (Dict[Any]): Weights stored locally.
+    """
+
+    def __init__(
+        self,
+        global_rank: int = 0,
+        host: str = "127.0.0.1",
+        port: str = 1234,
+    ) -> None:
+        """Class init method
+
+        Args:
+            global_rank (int, optional): The rank in training process. Defaults to 0.
+            host (str, optional): Host ip address. Defaults to '127.0.0.1'.
+            port (str, optional): Port. Defaults to 1234.
+        """
+        self.host = host
+        self.port = port
+        self.global_rank = global_rank
+
+        self.sock = socket.socket()
+
+        self.weights = {}
+
+        self.zkp_client = None
+        self.check_param = None
+        self.random_bytes_str = None
+
+    def __start_connection(self) -> None:
+        """Start the network connection to server."""
+        self.sock.connect((self.host, self.port))
+
+    def __start_rank_pairing(self) -> None:
+        """Sending global rank to server"""
+        utils.send_int(self.sock, self.global_rank)
+
+    def init_zkp_client(self, args, global_model) -> None:
+        """Initialize zkp params etc."""
+        defense_type = check_defense_type(args.check_type)
+
+        sign_pub_keys_vec = risefl_interface.VecSignPubKeys(args.num_clients + 1)
+        print("****** [client_zkp.init_zkp_client] args.num_clients + 1 = ", args.num_clients + 1)
+        for j in range(args.num_clients + 1):
+            # print("j = ", j)
+            recv_pub_key_j_str = receive_string(self.sock)
+            sign_pub_keys_vec[j] = risefl_interface.convert_string_to_sign_pub_key(recv_pub_key_j_str)
+            # print("recv " + str(j) + " sign_pub_key success")
+        # print("recv_pub_key finished")
+        recv_prv_key_str = receive_string(self.sock)
+        # print("recv_prv_key_str = ", recv_prv_key_str)
+        sign_prv_keys_vec_i = risefl_interface.convert_string_to_sign_prv_key(recv_prv_key_str)
+
+        print("****** [client_zkp.init_zkp_client] init sign_keys success")
+
+        dim = int(flattened_weight_size(global_model))
+        print("****** [client_zkp.init_zkp_client] dim = ", dim)
+
+        # the client id in the zkp library starts from 1, so the index needs to be increased by 1
+        self.zkp_client = risefl_interface.ClientInterface(
+            args.num_clients, args.max_malicious_clients, dim,
+            args.num_blinds_per_group_element, args.weight_bits, args.random_normal_bit_shifter,
+            args.num_norm_bound_samples, args.inner_prod_bound_bits, args.max_bound_sq_bits,
+            defense_type, self.global_rank + 1,
+            sign_pub_keys_vec, sign_prv_keys_vec_i)
+
+        print(f"****** [client_zkp.init_zkp_client] self.global_rank + 1 = {self.global_rank + 1}")
+        print("****** [client_zkp.init_zkp_client] create zkp_client success")
+
+        # initialize the check parameter
+        self.check_param = risefl_interface.CheckParamFloat(defense_type)
+        self.check_param.l2_param.bound = args.norm_bound
+
+        # a random string used to generate independent group elements, to be used by both the server and clients
+        # random_bytes = os.urandom(64)
+        # random_bytes_str = base64.b64encode(random_bytes).decode('ascii')
+        # random_bytes_str = "r0sdTz/eXbBDsPpB9QiB4P+ejll9juZdbYa4Xt+OZbFlV/n7FUcTMas64getSoWMoV5hE+UmiR6W554xa4SPnQ=="
+        # print("random_bytes_str = " + random_bytes_str)
+        self.random_bytes_str = receive_string(self.sock)
+        self.zkp_client.initialize_from_seed(self.random_bytes_str)
+        print("****** [client_zkp.init_zkp_client] received random_bytes_str from server")
+        print("****** [client_zkp.init_zkp_client] init zkp_client success")
+
+    def start(self) -> None:
+        """Start the client.
+
+        This method will first connect to the server. Then global rank is sent to the server.
+        """
+        self.__start_connection()
+        self.__start_rank_pairing()
+
+        print(f"[Client {self.global_rank}] Connect to {self.host}:{self.port}")
+
+    def close(self) -> None:
+        """Close the server."""
+        self.sock.close()
+
+    def pull(self) -> None:
+        """Client pull weights from server.
+
+        Namely server push weights from clients.
+        """
+        message = proto.WeightsExchange()
+        message = utils.receive_message(self.sock, message)
+        for k, v in message.weights.items():
+            self.weights[k] = utils.deserialize_tensor(v)
+
+    def push(self) -> None:
+        """Client push weights to server.
+
+        Namely server pull weights from clients.
+        """
+        message = proto.WeightsExchange()
+        message.op_type = proto.GATHER
+        for k, v in self.weights.items():
+            message.weights[k] = utils.serialize_tensor(v)
+        utils.send_message(self.sock, message)
+
+
+def run(args, device_id):
+    # Connect to server
+    client = Client(global_rank=device_id)
+    client.start()
+
+    # if args.gpu_id:
+    #     torch.cuda.set_device(args.gpu_id)
+    device = 'cuda' if args.gpu else 'cpu'
+
+    # load dataset and user groups
+    train_dataset, test_dataset = get_dataset(args.data, args.data_dir, client_id=client.global_rank)
+
+    # BUILD MODEL
+    if args.model == 'cnn':
+        # Convolutional neural netork
+        if args.data == 'mnist':
+            global_model = CNNMnist(num_channels=args.num_channels, num_classes=args.num_classes)
+
+    elif args.model == 'mlp':
+        # Multi-layer perceptron
+        len_in = args.num_features
+        global_model = MLP_Bank(dim_in=len_in, dim_hidden=64, dim_out=args.num_classes)
+    else:
+        exit('****** [client_zkp.run] Error: unrecognized model')
+
+    # Set the model to train and send it to device.
+    global_model.to(device)
+    global_model.train()
+    print(global_model)
+
+    # copy weights
+    global_weights = global_model.state_dict()
+    client.weights = global_weights
+
+    client.init_zkp_client(args, global_model)
+
+    # Training
+    train_loss, train_accuracy = [], []
+
+    for epoch in tqdm(range(args.max_epoch)):
+        # local_weights, local_losses = [], []
+        print(f'\n | Global Training Round : {epoch} |\n')
+
+        global_model.load_state_dict(client.weights)
+
+        print("****** [client_zkp.run] client start local update...")
+        local_model = LocalUpdate(args=args, dataset=train_dataset)
+        w, loss = local_model.update_weights(
+                model=copy.deepcopy(global_model), global_round=epoch)
+        print("****** [client_zkp.run] client finish local update...")
+
+        client.weights = copy.deepcopy(w)
+        print(f'****** [client_zkp.run] Local training loss : {loss}')
+
+        # step 1 client sends message to the server
+        # flatten weights to 1D array
+        print(f"****** [client_zkp.run] client.weights.type: {type(client.weights)}")
+        # print("client.weights: ", client.weights)
+        flatten_weights = flatten_model_weights(w)
+        # clip the weight by norm_bound, otherwise, will get error when the server aggregates
+        flatten_weights *= args.norm_bound / np.linalg.norm(flatten_weights)
+        flatten_weights = flatten_weights.tolist()
+        print(f"****** [client_zkp.run] flatten_weights.type: {type(flatten_weights)}")
+        print(f"****** [client_zkp.run] flatten_weights.length: {len(flatten_weights)}")
+        # print("flatten_weights: ", flatten_weights)
+
+        # for testing the correctness of summation when dim = 2
+        # weight_updates_collection = [[0, 0], [0.09574025869369507, -0.0437011756002903],
+        #                             [-0.012869355268776417, 0.0022518674377352],
+        #                             [-0.07237587869167328, 0.12259631603956223]]
+        # weight_updates_collection = np.random.rand(4, 31)
+        # weight_updates_collection = weight_updates_collection.tolist()
+        # print(f"weight_update_collection.type: {type(weight_updates_collection[1])}")
+        # print(weight_updates_collection[client.global_rank + 1])
+        # print(f"flatten_weights: {flatten_weights}")
+        converted_weights = risefl_interface.VecFloat(flatten_weights)
+        # converted_weights = risefl_interface.VecFloat(weight_updates_collection[client.global_rank + 1])
+        client_send_str1 = client.zkp_client.send_1(client.check_param, converted_weights)
+        # send this string to the server
+        send_string(client.sock, client_send_str1)
+
+        print("****** [client_zkp.run] client_sends_str1 finished")
+
+        # step 2 receive message from the server and sends message back to the server
+        server_sent_2_str = receive_string(client.sock)
+        # bytes_sent_2 = sent_2.encode()
+        client_send_str2 = client.zkp_client.receive_and_send_2(server_sent_2_str)
+        send_string(client.sock, client_send_str2)
+
+        print("****** [client_zkp.run] client_sends_str2 finished")
+
+        # step 3 receive message from the server and sends message back to the server
+        server_sent_3_str = receive_string(client.sock)
+        client_send_str3 = client.zkp_client.receive_and_send_3(server_sent_3_str)
+        send_string(client.sock, client_send_str3)
+
+        print("****** [client_zkp.run] client_sends_str3 finished")
+
+        # step 4 receive message from the server and sends message back to the server
+        server_sent_4_str = receive_string(client.sock)
+        client_send_str4 = client.zkp_client.receive_and_send_4(server_sent_4_str)
+        send_string(client.sock, client_send_str4)
+
+        print("****** [client_zkp.run] client_sends_str4 finished")
+
+        # step 5 receive message from the server and sends message back to the server
+        server_sent_5_str = receive_string(client.sock)
+        client_send_str5 = client.zkp_client.receive_and_send_5(server_sent_5_str)
+        send_string(client.sock, client_send_str5)
+
+        print("****** [client_zkp.run] client_sends_str5 finished")
+
+        # pull the latest round of model weights
+        client.pull()
+        print(f"****** [client_zkp.run] client finishes pulling weights from server")
+
+        local_model = LocalUpdate(args=args, dataset=train_dataset)
+        acc, loss = local_model.inference(model=global_model)
+        train_accuracy.append(acc)
+        train_loss.append(loss)
+        print("|---- [client_zkp.run] Train Accuracy: {:.2f}%".format(100*acc))
+        print("|---- [client_zkp.run] Train Loss: {:.2f}".format(loss))
+
+    print(f"****** [client_zkp.run] Train Accuracy: {train_accuracy}")
+    print(f"****** [client_zkp.run] Train Loss: {train_loss}")
+
+    # Test inference after completion of training
+    test_acc, test_loss = test_inference(args, global_model, test_dataset)
+
+    print(f' \n Results after {args.max_epoch} global rounds of training:')
+    print("|---- [client_zkp.run] Test Accuracy: {:.2f}%".format(100*test_acc))
+    print("|---- [client_zkp.run] Test Loss: {:.2f}".format(test_loss))
+
+    client.close()
+
+
+if __name__ == "__main__":
+    args = parseargs()
+    run(args, args.device_id)