Fairness and privacy preserving in federated learning: A survey

Taki Hasan Rafi, Faiza Anan Noor, Tahmid Hussain, Dong-Kyu Chae

Abstract: Highlights•First comprehensive survey reviewing privacy-preserving and fairness in federated learning (FL) together.•Broad outline of recent privacy and fairness methods, challenges, and relevant works in FL.•Investigation of privacy concerns in FL, evaluating methods, and summarizing common evaluation metrics.•Exploration of fairness in FL systems, identifying factors, challenges, and in-depth evaluation metrics.•Identification of potential challenges and future directions for preserving privacy and fairness in FL.