4. Configuration

Configuring and Setting Up the Federated Learning Environment

Understanding the Configuration File

Cifer's FedLearn uses a config.json file located in the root directory of the project for configuration. This file contains all necessary settings for both local testing and distributed federated learning.

To locate the file:

bash

cd path/to/cifer
ls config.json

Configuration Parameters

The config.json file includes the following key parameters:

• server_address: The gRPC server address and port (e.g., "localhost:8080").

• num_rounds: Number of federated learning rounds.

• strategy: The federated learning strategy (e.g., "FedAvg" for Federated Averaging).

• grpc_max_message_length: Maximum gRPC message size in bytes.

• mode: "local" for single-machine simulation, "collaborative" for distributed setup.

• min_num_clients: Minimum number of clients required for a round (in collaborative mode).

• min_sample_size: Minimum number of samples required from each client.

• model_name: Name of the model to be used.

• local_epochs: Number of local training epochs per round.

• batch_size: Batch size for local training.

• learning_rate: Learning rate for local training.

• data_dir: Directory containing the dataset.

• output_dir: Directory for saving output files.

Local Mode Configuration

For local testing and development, use these settings:

json

{
  "server_address": "localhost:8080",
  "num_rounds": 10,
  "strategy": "FedAvg",
  "grpc_max_message_length": 104857600,
  "mode": "local",
  "num_clients": 3,
  "local_epochs": 5,
  "batch_size": 32,
  "learning_rate": 0.01,
  "model_name": "simple_cnn",
  "dataset": "mnist",
  "data_dir": "./data",
  "output_dir": "./output"
}

Collaborative Mode Configuration

For distributed federated learning, modify the configuration as follows:

json

{
  "server_address": "0.0.0.0:8080",
  "num_rounds": 50,
  "strategy": "FedAvg",
  "grpc_max_message_length": 209715200,
  "mode": "collaborative",
  "min_num_clients": 3,
  "min_sample_size": 1000,
  "model_name": "resnet18",
  "local_epochs": 5,
  "batch_size": 64,
  "learning_rate": 0.001,
  "data_dir": "./local_data",
  "output_dir": "./collaborative_output"
}

Setting Up the Server

1. Choose a machine with good network connectivity and sufficient resources.

2. Update the config.json on the server with appropriate settings (use the collaborative mode configuration as a starting point).

3. Start the server:

python

from cifer import fedlearn

# Initialize and start FedLearn server
fedlearn.start_server('config.json')

Setting Up Clients

For each participating client:

1. Install Cifer and ensure access to the local dataset.

2. Update the config.json on each client, changing the server_address to match the server's address.

3. Start the client:

python

from cifer import fedlearn

# Initialize and start FedLearn client
fedlearn.start_client('config.json')

Network and Security Considerations

• Ensure the server's firewall allows incoming connections on the specified port.

• If using secure gRPC, generate and distribute SSL/TLS certificates, and update configurations accordingly.

Data Preparation and Model Selection

• Ensure each client has access to its local dataset in the specified data_dir.

• Verify data format consistency across all clients.

• Choose an appropriate model_name in the configuration, or implement and specify a custom model.

Monitoring and Logging

• Monitor server output for overall progress and issues.

• Check individual client logs for local training progress and client-specific problems.

Troubleshooting Common Issues

• Connection Problems: Verify network settings and firewall configurations.

• Data Issues: Ensure correct data format and sufficient samples on all clients.

• Resource Constraints: Adjust batch_size and local_epochs if clients experience memory issues or slow processing.

• Model Compatibility: Verify that the chosen model is compatible with the dataset and task.

Participant Roles and Responsibilities

In a federated learning environment, different participants have distinct roles and responsibilities:

1. Server Administrator:

• Set up and maintain the central server

• Configure global parameters (rounds, minimum clients, etc.)

• Monitor the overall federated learning process

• Ensure security and privacy protocols are followed

• Manage model aggregation and distribution

2. Client Participants:

• Prepare and maintain local datasets

• Ensure local system meets hardware and software requirements

• Run client software and participate in training rounds

• Protect the privacy of local data

• Report any issues to the server administrator

3. Data Scientists/ML Engineers:

• Design and implement the shared model architecture

• Define evaluation metrics for the federated model

• Analyze aggregated results and refine the model as needed

• Collaborate with server administrator on hyperparameter tuning

4. Privacy Officers (if applicable):

• Review and approve the federated learning setup

• Ensure compliance with data protection regulations

• Monitor the process for potential privacy risks

• Advise on implementing additional privacy-preserving techniques

5. IT Support:

• Assist with network configuration and troubleshooting

• Ensure proper firewall settings and network security

• Help with software installation and updates on client machines

6. Project Manager:

• Coordinate between different participants

• Ensure timely execution of federated learning rounds

• Manage communication between technical and non-technical stakeholders

By clearly defining these roles and responsibilities, organizations can ensure a smooth and effective federated learning process while maintaining data privacy and security.