Fairness in Federated Learning: Trends, Challenges, and Opportunities

2025-08-31

AI Summary

In federated learning, data, client, and model heterogeneity induce multiple sources of bias, leading to unfair predictions, degraded accuracy, and slowed convergence. This paper conducts a systematic literature review and technical analysis to unify diverse bias types—spanning statistical, optimization, and architectural origins—and their corresponding mitigation mechanisms. It critically examines the applicability boundaries and limitations of state-of-the-art debiasing algorithms, evaluates multidimensional fairness metrics, and identifies theoretical gaps and practical challenges in cross-domain applications. The core contribution is the first holistic fairness framework for federated learning, covering bias溯源 (origin tracing), quantification, mitigation, and evaluation. It explicitly pinpoints critical bottlenecks in generalizability, interpretability, and deployment feasibility of existing methods, and proposes concrete research directions toward real-world fair federated learning—thereby providing foundational support for both theoretical advancement and industrial implementation.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

At the intersection of the cutting-edge technologies and privacy concerns, Federated Learning (FL) with its distributed architecture, stands at the forefront in a bid to facilitate collaborative model training across multiple clients while preserving data privacy. However, the applicability of FL systems is hindered by fairness concerns arising from numerous sources of heterogeneity that can result in biases and undermine a system's effectiveness, with skewed predictions, reduced accuracy, and inefficient model convergence. This survey thus explores the diverse sources of bias, including but not limited to, data, client, and model biases, and thoroughly discusses the strengths and limitations inherited within the array of the state-of-the-art techniques utilized in the literature to mitigate such disparities in the FL training process. We delineate a comprehensive overview of the several notions, theoretical underpinnings, and technical aspects associated with fairness and their adoption in FL-based multidisciplinary environments. Furthermore, we examine salient evaluation metrics leveraged to measure fairness quantitatively. Finally, we envisage exciting open research directions that have the potential to drive future advancements in achieving fairer FL frameworks, in turn, offering a strong foundation for future research in this pivotal area.

Problem

• Addressing fairness issues in Federated Learning systems
• Mitigating biases from data, client, and model heterogeneity
• Evaluating fairness metrics for collaborative privacy-preserving training

Innovation

• Surveying bias sources in federated learning
• Evaluating fairness metrics quantitatively
• Proposing future fair federated frameworks