diff --git a/src/evolution/distributed_node.py b/src/evolution/distributed_node.py
new file mode 100644
index 00000000..036cb408
--- /dev/null
+++ b/src/evolution/distributed_node.py
@@ -0,0 +1,168 @@
+from enum import Enum, auto
+from typing import Any
+from hyperon_das_node import StarNode, QueryAnswer
+
+from src.evolution.utils import Parameters, parse_file
+
+
+class State(Enum):
+    INITIAL = auto()
+    SAMPLING = auto()
+    WAITING = auto()
+    NETWORK_UPDATE = auto()
+    FINISHED = auto()
+
+
+class DistributedEvolutionNode(StarNode):
+    def __init__(self, node_id: str, server_id: str = None, config_file: str = None) -> None:
+        self.state = State.INITIAL
+        self.population = []
+        self.remote_population = {}
+        self.joinin_requests_queue = set()
+        self.params = None
+        if server_id is None:
+            if not config_file:
+                raise ValueError("For server-type nodes, a configuration file ('config_file') must be provided.")
+            self.params = self._load_config(config_file)
+            self.current_generation = 1
+            super().__init__(node_id)
+        else:
+            super().__init__(node_id, server_id)
+
+    def transition(self, new_state: State) -> None:
+        self.state = new_state
+
+    def _load_config(self, config_file: str) -> Parameters:
+        """
+        Loads and validates configuration parameters from the specified file.
+
+        Raises:
+            ValueError: If the number of individuals selected for attention update exceeds population size.
+        """
+        config = parse_file(config_file)
+        params = Parameters(**config)
+        if params.qtd_selected_for_attention_update > params.population_size:
+            raise ValueError(
+                "The number of selected individuals for attention update cannot be greater than the population size."
+            )
+        return params
+
+    def node_joined_network(self, node_id: str) -> None:
+        if self.is_leader():
+            self.joinin_requests_queue.add(node_id)
+
+            # For now all nodes are accepted
+            self._accept_join_request(node_id)
+
+    def _accept_join_request(self, node_id: str) -> None:
+        if node_id in self.joinin_requests_queue:
+            self.joinin_requests_queue.remove(node_id)
+        super().node_joined_network(node_id)
+
+    def run(self) -> None:
+        """
+        Run the node's control state machine in a loop until the stopping criterion is met.
+        """
+        if self.state == State.INITIAL:
+            self.initialize()
+            self.transition(State.SAMPLING)
+
+        # Main loop for processing generations.
+        while not self.stop_criteria():
+            if self.state == State.SAMPLING:
+                self.sample_population()
+                self.transition(State.WAITING)
+            if self.state == State.WAITING:
+                self.wait_for_others()
+                self.transition(State.NETWORK_UPDATE)
+            if self.state == State.NETWORK_UPDATE:
+                self.network_update()
+                self.transition(State.SAMPLING)
+            
+            if self.is_leader():
+                self.current_generation += 1
+
+    def current_state(self) -> dict[str, Any]:
+        """
+        Retrieve the current state of the node.
+
+        Returns:
+            dict[str, Any]: A dictionary representing the current state of the node,
+                including relevant attributes such as population, remote population, and
+                other state-related information.
+        """
+
+    def stop_criteria(self) -> bool:
+        """
+        Determine whether the stopping criterion is met.
+
+        Returns:
+            bool: True if the stopping criterion is met, False otherwise.
+        """
+
+    def initialize(self) -> None:
+        """
+        Start a new generation by transitioning the node's state and
+        broadcasting a start signal if the node is the leader.
+        """
+
+    def sample_population(self) -> None:
+        """
+        Sample the local population by performing a query, computing fitness,
+        and broadcasting each individual.
+        """
+
+    def perform_query(self) -> QueryAnswer:
+        """
+        Perform a query to sample a new QueryAnswer.
+
+        Returns:
+            QueryAnswer: A new query answer object.
+        """
+
+    def compute_fitness(self, query_answer: QueryAnswer) -> float:
+        """
+        Compute the fitness value for a given QueryAnswer.
+
+        Args:
+            query_answer (QueryAnswer): The query answer to evaluate.
+
+        Returns:
+            float: The computed fitness value.
+        """
+
+    def wait_for_others(self) -> None:
+        """
+        Wait for other nodes to finish sending their individuals.
+        Process incoming messages and update the remote population data.
+        """
+
+    def check_all_nodes_received(self) -> bool:
+        """
+        Check if all live nodes have sent the required number of individuals.
+        
+        Returns:
+            bool: True if all nodes have sent their individuals, False otherwise.
+        """
+
+    def spread_activation(self) -> None:
+        """
+        Leader node selects individuals from the combined populations and sends
+        stimuli using an AttentionBroker mechanism.
+        """
+
+    def send_stimuli(self) -> None:
+        """
+        Send stimuli using the AttentionBroker.
+        """
+
+    def reset_populations(self) -> None:
+        """
+        Reset the local and remote populations to prepare for the next generation.
+        """
+
+    def network_update(self) -> None:
+        """
+        Update the network by processing joining requests. The leader sends
+        node lists to new nodes and informs all nodes about the new node.
+        """