Abstract Federated learning (FL) enables multi-institutional collaboration in medical imaging while preserving patient privacy, yet its fairness landscape remains fragmented: existing methods predominantly address either collaboration fairness (equitable performance across institutions) or group fairness (equitable outcomes across demographic subgroups), but rarely both. In this systematic review, we adopt dual fairness —the joint satisfaction of both dimensions—as the analytical lens for organizing and critically evaluating this landscape. Following the PRISMA 2020 guidelines, we analyze 132 publications and classify fairness-aware FL methods through a three-dimensional taxonomy: client-side, server-side, and communication-based approaches. Among the 20 fairness-aware or fairness-adapted FL methods catalogued, only three partially address both dimensions, and none provides provable joint guarantees under clinically realistic conditions. Our critical analysis identifies three fundamental challenges: the Local–Global Pareto Frontier Conflict, in which collaboration and group fairness gradients in the accuracy space can exceed 150^ under sufficiently asymmetric demographic imbalance (e. g. , ₁ 0. 8) ; the Privacy–Fairness Compounding Effect, through which differential privacy mechanisms disproportionately suppress minority gradient signals; and the risk of pseudo-fairness, whereby equipment–demographic confounding masks genuine algorithmic discrimination. We further outline a seven-direction research roadmap. To the best of our knowledge, this constitutes the first systematic review to formally analyze the gradient-level conflict between collaboration fairness and group fairness in federated medical imaging, while also providing a structured causal analysis of equipment–demographic confounding, offering both a critical synthesis and actionable directions toward equitable AI-assisted healthcare.
Yu et al. (2026) studied this question.