Algorithmic Fairness via Differential Privacy and Federated Learning

Abstract

Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning models, particularly in scenarios where models are trained on diverse and potentially biased datasets. Traditional approaches to achieving fairness often rely on centralized data collection and model training, which can be impractical, raise significant privacy concerns, and exacerbate existing biases due to skewed data distributions. This paper proposes a novel framework for achieving algorithmic fairness in distributed learning environments by integrating differential privacy with federated learning. Our approach allows individual clients to contribute data to a global model without revealing their sensitive information, while simultaneously mitigating bias introduced by biased data distributions. We leverage the privacy guarantees of differential privacy to protect client data and use federated learning to aggregate model updates from multiple clients. The core of our method involves carefully tuning the differential privacy parameters and adapting the federated learning algorithm to account for potential biases in the underlying data. The resulting model exhibits improved fairness metrics compared to traditional federated learning approaches. We demonstrate the effectiveness of our framework through a theoretical analysis and a simulation study. The primary contribution of this work is a practical and privacy-preserving method for fostering algorithmic fairness in decentralized learning systems.