FedFDP: Fairness-Aware Federated Learning with Differential Privacy

Understand

Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention.

• However, FL encounters persistent issues related to fairness and data privacy.
• To tackle these challenges simultaneously, we propose a fairness-aware federated learning algorithm called FedFair.
• Building on FedFair, we introduce differential privacy to create the FedFDP algorithm, which addresses trade-offs among fairness, privacy protection, and model performance.