FLWR Federated Learning Simulation Project
Budget: $10 – $30 USD
Create a complete Flower (flwr) federated learning simulation project to analyze and compare the performance of different federated learning algorithms. The project should include:
PROJECT STRUCTURE:
- Complete pyproject.toml configuration (hyper paraeters , like i should choose how uch clients .... )
- Client implementation with multiple FL algorithms
- Server implementation with different aggregation strategies
- Performance analysis and visualization tools
- Dataset handling (CIFAR-10 , MNIST, cifar 100, shacspire, femnist, ...)
- Comprehensive logging and metrics collection
REQUIREMENTS:
1. ALGORITHMS TO IMPLEMENT:
- FedAvg (Federated Averaging)
- FedProx (Federated Proximal)
- FedAdam (Federated Adam optimizer)
- fed yogi , scaffold, coop 2. CLIENT FEATURES:
- Configurable model architectures (CNN, ResNet and much more)
- Data partitioning (IID and non-IID scenarios)
- Local training with different optimizers
- Client dropout simulation
- Heterogeneous client capabilities
3. SERVER FEATURES:
- Multiple aggregation strategies
- Adaptive learning rates
- Client selection mechanisms
- Round-based training control
- Convergence detection
4. PERFORMANCE METRICS:
- Training/validation accuracy over rounds
- Communication costs (bytes transferred)
- Training time per round
- Model convergence speed
- Fairness metrics across clients
- Resource utilization
5. ANALYSIS TOOLS:
- Real-time plotting of metrics
- Comparative analysis between algorithms in term of handling heterogeniety, communication and privacy
- Statistical significance testing
- Export results to CSV/JSON
- Generate performance reports, accuracy, loss, model drift, communication cost, convergence,
6. CONFIGURATION:
- YAML/TOML config files for hyperparameters
- Experiment reproducibility (seeds)
- Scalable client numbers (2-20 clients)
- Different data distribution scenarios
7. FILES TO CREATE:
- pyproject.toml (Flower configuration)
- client_app.py (client logic)
- server_app.py (server logic)
- strategy.py (custom strategies)
- models.py (neural network architectures)
- utils.py (helper functions)
- data_utils.py (dataset handling)
- analysis.py (performance analysis)
- visualization.py (plotting and reports)
- config.yaml (experiment parameters)
- requirements.txt (dependencies)
8. SIMULATION SCENARIOS:
- IID data distribution
- Non-IID data distribution (label skew)
- System heterogeneity simulation
- Network latency simulation
- Client availability patterns
TECHNICAL SPECIFICATIONS:
- Use PyTorch for models
- Support both CPU and GPU training
- Implement proper error handling
- Include comprehensive docstrings
- Add type hints throughout
- Create unit tests for key components
- Support Windows, Linux, macOS
DELIVERABLES:
- Working flwr simulation that runs with flwr run .
- Automated experiment runner
- Performance comparison dashboard
- Detailed README with setup instructions
- Example configuration files
- Sample results and analysis
Make the code modular, well-documented, and production-ready. Include example experiments that demonstrate the performance differences between federated learning algorithms under various conditions.
PROJECT STRUCTURE:
- Complete pyproject.toml configuration (hyper paraeters , like i should choose how uch clients .... )
- Client implementation with multiple FL algorithms
- Server implementation with different aggregation strategies
- Performance analysis and visualization tools
- Dataset handling (CIFAR-10 , MNIST, cifar 100, shacspire, femnist, ...)
- Comprehensive logging and metrics collection
REQUIREMENTS:
1. ALGORITHMS TO IMPLEMENT:
- FedAvg (Federated Averaging)
- FedProx (Federated Proximal)
- FedAdam (Federated Adam optimizer)
- fed yogi , scaffold, coop 2. CLIENT FEATURES:
- Configurable model architectures (CNN, ResNet and much more)
- Data partitioning (IID and non-IID scenarios)
- Local training with different optimizers
- Client dropout simulation
- Heterogeneous client capabilities
3. SERVER FEATURES:
- Multiple aggregation strategies
- Adaptive learning rates
- Client selection mechanisms
- Round-based training control
- Convergence detection
4. PERFORMANCE METRICS:
- Training/validation accuracy over rounds
- Communication costs (bytes transferred)
- Training time per round
- Model convergence speed
- Fairness metrics across clients
- Resource utilization
5. ANALYSIS TOOLS:
- Real-time plotting of metrics
- Comparative analysis between algorithms in term of handling heterogeniety, communication and privacy
- Statistical significance testing
- Export results to CSV/JSON
- Generate performance reports, accuracy, loss, model drift, communication cost, convergence,
6. CONFIGURATION:
- YAML/TOML config files for hyperparameters
- Experiment reproducibility (seeds)
- Scalable client numbers (2-20 clients)
- Different data distribution scenarios
7. FILES TO CREATE:
- pyproject.toml (Flower configuration)
- client_app.py (client logic)
- server_app.py (server logic)
- strategy.py (custom strategies)
- models.py (neural network architectures)
- utils.py (helper functions)
- data_utils.py (dataset handling)
- analysis.py (performance analysis)
- visualization.py (plotting and reports)
- config.yaml (experiment parameters)
- requirements.txt (dependencies)
8. SIMULATION SCENARIOS:
- IID data distribution
- Non-IID data distribution (label skew)
- System heterogeneity simulation
- Network latency simulation
- Client availability patterns
TECHNICAL SPECIFICATIONS:
- Use PyTorch for models
- Support both CPU and GPU training
- Implement proper error handling
- Include comprehensive docstrings
- Add type hints throughout
- Create unit tests for key components
- Support Windows, Linux, macOS
DELIVERABLES:
- Working flwr simulation that runs with flwr run .
- Automated experiment runner
- Performance comparison dashboard
- Detailed README with setup instructions
- Example configuration files
- Sample results and analysis
Make the code modular, well-documented, and production-ready. Include example experiments that demonstrate the performance differences between federated learning algorithms under various conditions.