FairSwarm

Provably fair particle swarm optimization for federated learning coalition selection

Overview

FairSwarm is a novel particle swarm optimization algorithm designed for fair client selection in federated learning. It provides provable guarantees on both convergence and demographic fairness.

Key Innovation

FairSwarm introduces a fairness-aware velocity update that steers optimization toward demographically balanced coalitions:

v = ω·v + c₁·r₁·(pBest - x) + c₂·r₂·(gBest - x) + c₃·∇_fair
                                                    ^^^^^^^^
                                                    Novel fairness gradient

Theoretical Guarantees

Theorem	Guarantee
Theorem 1	Convergence to stationary point with probability 1
Theorem 2	ε-fairness: DemDiv(S*) ≤ ε with high probability
Theorem 3	(1-1/e-η) approximation for submodular objectives
Theorem 4	Privacy-fairness tradeoff lower bound

Installation

pip install fairswarm

For development:

pip install fairswarm[dev]

Quick Start

from fairswarm import FairSwarm, FairSwarmConfig, Client
from fairswarm.demographics import DemographicDistribution, CensusTarget
import numpy as np

# Create clients (hospitals) with demographic information
clients = [
    Client(
        id=f"hospital_{i}",
        demographics=np.random.dirichlet([2, 2, 2, 2]),
        dataset_size=1000 + i * 100,
    )
    for i in range(20)
]

# Configure the optimizer
config = FairSwarmConfig(
    swarm_size=30,
    max_iterations=100,
    coalition_size=10,
    fairness_weight=0.3,  # λ in fitness function
    seed=42,
)

# Create target demographics (e.g., US Census 2020)
target = DemographicDistribution.from_dict({
    "white": 0.576,
    "black": 0.124,
    "hispanic": 0.187,
    "asian": 0.061,
    "other": 0.052,
})

# Run optimization
optimizer = FairSwarm(
    clients=clients,
    coalition_size=10,
    target_demographics=target,
    config=config,
)
# Use built-in demographic fitness (or supply your own callable)
from fairswarm.fitness import DemographicFitness
fitness_fn = DemographicFitness(target=target)

result = optimizer.optimize(fitness_fn)

# Check results
print(f"Selected coalition: {result.coalition}")
print(f"Fitness: {result.fitness:.4f}")
print(f"ε-fair: {result.is_epsilon_fair(0.05)}")

Documentation

Full API documentation is available in the source code docstrings. For the formal algorithm specification, theoretical proofs, and experimental methodology, please refer to:

T. Norwood, D. Das, P. Chatterjee, E. Bentley, and U. Ghosh, "FairSwarm: Trustworthy Coalition Selection for Fair and Secure Federated Intelligence," IEEE Trans. Consum. Electron., 2026. note : Submitted

Citation

@phdthesis{norwood2026fairswarm,
  title={FairSwarm: A Provably Fair Particle Swarm Optimization Algorithm
         for Federated Learning Coalition Selection with Applications in Healthcare},
  author={Norwood, Tenicka},
  year={2026},
  school={Meharry Medical College}
}

License

This project is licensed under the PolyForm Noncommercial License 1.0.0. You are free to use, modify, and distribute it for any noncommercial purpose, including academic research, education, and personal projects.

Commercial licensing is available. For commercial use inquiries, please contact Tenicka Norwood.

See LICENSE for full terms.