Fairness-aware Agnostic Federated Learning

Understand

Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices.

• Most of the previous works about federated learning focus on the privacy protection and communication cost reduction.
• However, how to achieve fairness in federated learning is under-explored and challenging especially when testing data distribution is different from training distribution or even unknown.
• Introducing simple fairness constraints on the centralized model cannot achieve model fairness on unknown testing data.