Worker Node

Introduction

The Worker Node is an essential component of a decentralized Synnq Ecosystem. It is responsible for processing transactions, training local neural network models, participating in governance, and synchronizing with other nodes in the network. This documentation provides an overview of the Worker Node's architecture, functionality, and how to effectively utilize its capabilities.

Architecture and Components

The Worker Node is designed with several key components:

• DAG (Directed Acyclic Graph): This is the structure used to record and manage transaction histories and their dependencies. It ensures that all transactions are processed in a consistent order without conflicts.

• Key Manager: Manages cryptographic keys for wallets. It handles secure operations like signing transactions and verifying signatures.

• Neural Network: The Worker Node contains a local neural network model that can be trained on transaction data. This model can be updated and synchronized with other nodes to ensure consistency and accuracy across the network.

• Simulation Module: Simulates scenarios for model training and prediction purposes, allowing the neural network to learn and improve over time.

• Reputation System: Tracks the performance and reliability of the Worker Node. Nodes with higher reputation scores are trusted more within the network, which influences their role in governance and transaction processing.

• Token Manager: Handles the creation, management, and distribution of tokens within the network. It ensures that all token-related operations are secure and compliant with network protocols.

Core Functionalities

1. Node Initialization

The Worker Node is initialized with various configuration options, such as the address of the primary node, neural network settings, and whether to use GPU for processing. During initialization, the node prepares its components, loads any necessary data, and gets ready to start processing.

2. Running the Worker Node

Once initialized, the Worker Node enters a loop where it continuously performs its core tasks:

• Transaction Processing: The node processes incoming transactions, updates its DAG, and ensures that all transactions are recorded accurately.

• Model Training: The Worker Node periodically trains its neural network using the data it has gathered. This training can be performed using either the CPU or GPU, depending on the node's configuration.

• Data Synchronization: The node regularly synchronizes its transaction history and model updates with the primary node and other worker nodes. This ensures that all nodes in the network have a consistent view of the data.

• Governance Participation: The Worker Node takes part in the network's governance processes, such as voting on proposals and contributing to decision-making. Nodes with higher reputation scores have more influence in governance.

• Data Gossiping: The Worker Node shares its latest state and updates with other nodes, helping to maintain network-wide consistency.

3. Model Training from File

The Worker Node can train its neural network using data from an external file. This is useful for scenarios where the node needs to learn from a predefined dataset or when bootstrapping the model with existing knowledge. The node reads the data, processes it, and updates its neural network accordingly.

4. Model Update and Synchronization

After training, the Worker Node generates a model update, which is then sent to the primary node. This update includes the latest changes to the neural network model, ensuring that the entire network benefits from the node's training efforts.

5. Reputation Management

The Worker Node's reputation is critical for its role in the network. Successful task completion, frequent participation in governance, and accurate model training all contribute to a higher reputation score. The node's reputation influences its standing in the network and its ability to participate in key decision-making processes.

Using the Worker Node

To effectively use the Worker Node:

• Initialization: Start by configuring the node with the necessary settings, including the primary node address, neural network parameters, and whether GPU support is enabled.

• Continuous Operation: Once initialized, allow the node to run continuously. It will handle transaction processing, model training, and synchronization automatically.

• Model Training: If needed, train the neural network using external data by providing a training file. This can help improve the node's performance and accuracy in processing transactions and making predictions.

• Monitor Performance: Regularly check the node's reputation and performance metrics to ensure it is functioning optimally within the network.

• Participate in Governance: Engage in the network's governance processes to maintain a high reputation and contribute to the overall decision-making.