psfed 0.1.1

Partial Model Sharing for Federated Learning - A PyTorch and Flower extension

PSFed: Partial Model Sharing for Federated Learning

PSFed is a research-grade Python package that implements partial model sharing in federated learning. Instead of communicating the entire model between server and clients, PSFed enables selective parameter synchronization based on configurable masking strategies.

Key Features

• 🎯 Parameter-level granularity: Share any subset of model parameters
• 🔀 Dynamic masking: Masks change per round, ensuring eventual full synchronization
• 📊 Multiple strategies: Random, top-k magnitude, gradient-based, and custom selectors
• 🌸 Flower integration: Drop-in strategy and client implementations
• 🔥 PyTorch native: Works with any nn.Module
• ⚡ Communication efficient: Reduce bandwidth by 50-90%

Installation

pip install psfed

Or install from source:

git clone https://github.com/ehsan-lari/psfed.git
cd psfed
pip install -e ".[dev]"

Quick Start

Basic Usage (Without Flower)

import torch
import torch.nn as nn
from psfed import FlattenedModel, RandomMaskSelector

# Your PyTorch model
model = nn.Sequential(
    nn.Linear(784, 128),
    nn.ReLU(),
    nn.Linear(128, 10)
)

# Wrap for partial sharing
flat_model = FlattenedModel(model)
print(f"Total parameters: {flat_model.num_parameters}")

# Create a mask selector (50% of parameters, changes each round)
selector = RandomMaskSelector(fraction=0.5, seed=42)

# Generate mask for round 1
mask = selector.select(flat_model.num_parameters, round_num=1)
print(f"Selected {mask.count} / {mask.size} parameters")

# Extract selected parameters (for sending to clients)
partial_params = flat_model.extract(mask)

# ... transmit partial_params to client ...

# Apply received parameters (on client side)
flat_model.apply(partial_params, mask)

Federated Learning with Flower

Server:

import flwr as fl
from psfed.flower import PSFedAvg

# Define strategy with partial sharing
strategy = PSFedAvg(
    fraction_fit=0.1,
    fraction_evaluate=0.1,
    min_fit_clients=2,
    min_available_clients=2,
    # PSFed-specific parameters
    mask_fraction=0.5,           # Share 50% of parameters
    mask_strategy="random",      # Per-round random selection
    mask_seed=42,                # Reproducibility
)

# Start server
fl.server.start_server(
    server_address="0.0.0.0:8080",
    config=fl.server.ServerConfig(num_rounds=10),
    strategy=strategy,
)

Client:

import flwr as fl
from psfed.flower import PSFedClient

class MyClient(PSFedClient):
    def __init__(self, model, trainloader):
        super().__init__(model)
        self.trainloader = trainloader
    
    def train_local(self, epochs: int = 1):
        # Your training logic here
        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)
        criterion = nn.CrossEntropyLoss()
        
        self.model.train()
        for _ in range(epochs):
            for images, labels in self.trainloader:
                optimizer.zero_grad()
                loss = criterion(self.model(images), labels)
                loss.backward()
                optimizer.step()

# Start client
client = MyClient(model, trainloader)
fl.client.start_client(server_address="127.0.0.1:8080", client=client)

Mask Selection Strategies

PSFed provides several built-in mask selection strategies:

Strategy	Description	Use Case
RandomMaskSelector	Per-round random selection	Default, ensures coverage over time
TopKMagnitudeSelector	Select largest parameters by absolute value	Focus on important weights
GradientBasedSelector	Select by gradient magnitude	Active/changing parameters
StructuredMaskSelector	Layer-aware selection	Preserve structure
FixedMaskSelector	User-defined indices	Full control

Custom Selector

from psfed import MaskSelector, Mask

class MyCustomSelector(MaskSelector):
    def select(
        self, 
        num_parameters: int, 
        round_num: int,
        **kwargs
    ) -> Mask:
        # Your selection logic
        indices = your_custom_logic(num_parameters, round_num)
        return Mask.from_indices(indices, size=num_parameters)

API Reference

Core Classes

• FlattenedModel: Wraps a PyTorch model for flatten/unflatten operations
• Mask: Binary mask with efficient storage and operations
• MaskSelector: Abstract base class for selection strategies

Flower Integration

• PSFedAvg: FedAvg strategy with partial model sharing
• PSFedClient: Base client class handling partial parameters

Research Background

This package implements the concept of partial model sharing in federated learning, where communication efficiency is achieved by transmitting only a subset of model parameters each round.

Key properties:

• Communication reduction: Proportional to (1 - mask_fraction)
• Convergence: Dynamic masking ensures all parameters are eventually synchronized
• Privacy: Non-shared parameters remain local

For theoretical analysis, see docs/research.md.

Examples

• MNIST Basic - Simple partial sharing example
• CIFAR-10 Advanced - Custom selectors and analysis

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

# Setup development environment
pip install -e ".[dev]"
pre-commit install

# Run tests
pytest

# Type checking
mypy src/psfed

# Linting
ruff check src/psfed

Citation

If you use PSFed in your research, please cite:

@article{lari2025resilience,
  title={Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing},
  author={Lari, Ehsan and Arablouei, Reza and Gogineni, Vinay Chakravarthi and Werner, Stefan},
  journal={IEEE Transactions on Signal and Information Processing over Networks},
  year={2025},
  publisher={IEEE}
}

License

MIT License - see LICENSE for details.