SYNNQ Federated Learning SDK (Beta)

The SYNNQ Federated Learning SDK provides a high-level interface for implementing federated learning systems. It encapsulates core functionality for client management, model training, and monitoring in a federated learning environment.

Features

• Client-Server Architecture: Implements both client and server components for federated learning
• Model Management: Supports federated model training and updates
• Security & Privacy: Built-in privacy management and security features
• Monitoring: Comprehensive metrics collection and dashboard visualization
• Configuration: Flexible configuration system for both clients and servers
• Logging: Integrated logging system for debugging and monitoring

Installation

Quick Start

Client Setup

from synnq_fl import FederatedClient, ClientConfig

# Initialize client configuration
config = ClientConfig(
    server_url="http://your-server-url",
    client_id="unique-client-id",
    model_path="path/to/model"
)

# Create and initialize client
client = FederatedClient(config)
client.initialize()

# Train with local data
training_data = {
    "features": [...],
    "labels": [...]
}
metrics = client.train(training_data)

Server Setup

from synnq_fl import FederatedServer, ServerConfig

# Initialize server configuration
config = ServerConfig(
    port=8080,
    num_clients=10,
    aggregation_rounds=100
)

# Create and start server
server = FederatedServer(config)
server.start()

Core Components