Towards Fairness-Aware Federated Learning

Abstract

Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL,and overlook the interests of the FL clients. This may result in unfair treatment of clients that discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey that helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest promising future research directions towards FAFL.

arXiv commentAccepted by IEEE Transactions on Neural Networks and Learning Systems

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Cited by in corpus (6)

• Federated learning-outcome prediction with multi-layer privacy protection
• Fairness in Federated Learning: Trends, Challenges, and Opportunities
• Federated Learning for Drowsiness Detection in Connected Vehicles
• FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
• A Framework for Verifiable and Auditable Federated Anomaly Detection
• FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks