Federated Learning at the Forefront of Fairness: A Multifaceted Perspective

2024-08-01

International Joint Conference on Artificial Intelligence

AI Summary

This work addresses the fairness challenges in federated learning arising from client heterogeneity, which often leads to uneven model performance across participants. To tackle this issue, the paper proposes a systematic taxonomy framework that unifies performance-oriented and capability-oriented fairness strategies, clarifying the technical pathways of existing approaches. Through a comprehensive literature review and taxonomic analysis, the authors construct a structured evaluation metric system for fairness, identifying key challenges and outlining promising directions for future research. This study provides a coherent theoretical foundation, a unified classification perspective, and a forward-looking roadmap to advance fairness-aware federated learning.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients’ constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.

Problem

• Federated Learning
• Fairness
• Heterogeneous Clients
• Model Performance
• Equity

Innovation

• Federated Learning
• Fairness
• Model Performance
• Client Capability
• Evaluation Metrics