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import argparse
import yaml
import logging
import logging.config
from pathlib import Path
from src.server.coordinator import FederatedCoordinator
from src.client.model import FederatedClient
def setup_logging(config):
"""Setup logging configuration."""
# Create logs directory if it doesn't exist
Path("logs").mkdir(exist_ok=True)
log_level = (config.get('monitoring', {}).get('log_level')
or config.get('server', {}).get('monitoring', {}).get('log_level')
or config.get('client', {}).get('monitoring', {}).get('log_level')
or 'INFO')
# Configure logging with UTF-8 encoding
logging.basicConfig(
level=log_level,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.StreamHandler(),
logging.FileHandler('logs/federated_learning.log', mode='a', encoding='utf-8')
]
)
# Reduce TensorFlow logging noise
logging.getLogger('tensorflow').setLevel(logging.WARNING)
# Create a divider in the log file
logger = logging.getLogger(__name__)
logger.info("\n" + "="*50)
logger.info("New Training Session Started")
logger.info("="*50 + "\n")
def load_config(config_path: str) -> dict:
with open(config_path, 'r') as f:
return yaml.safe_load(f)
def main():
parser = argparse.ArgumentParser(description='Federated Learning Demo')
parser.add_argument('--mode', choices=['server', 'client'], required=True)
parser.add_argument('--config', type=str, required=True)
args = parser.parse_args()
config = load_config(args.config)
setup_logging(config)
logger = logging.getLogger(__name__)
if args.mode == 'server':
coordinator = FederatedCoordinator(config)
logger.info("Starting server...")
coordinator.start()
else:
client = FederatedClient(1, config)
logger.info(f"Starting client with ID: {client.client_id}")
client.start()
if __name__ == "__main__":
main() |