首篇从数据视角系统解析联邦学习收敛问题的综述
From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
- 按数据异构性影响强度分级，揭示其对收敛的机制
- 发现数据划分方法会引入人为偏差，影响模型准确率
- 对比防御策略在正常与对抗场景下的收敛表现
联邦学习（FL）为集中式学习中的数据饥渴问题提供了有前景的解决方案，使多个客户端在不暴露本地数据的前提下协同训练共享模型。然而，数据既是核心组件，也是主要脆弱点和挑战源，直接决定训练的稳定性和收敛性。现有联邦学习综述多聚焦于基础原理、安全实践、机遇与应用，缺乏从数据角度深入分析的系统性梳理，尤其缺少将具体数据特性、划分协议与防御措施与收敛速度和稳定性关联起来的综合视角。本文填补这一空白，提出三项进展：第一，将非独立同分布（non-IID）分解为可度量属性，并按对收敛的影响强度（强、中、弱）排序，解释其背后机制并整合图像、文本、图数据的证据；第二，揭示实验性数据划分方式所模拟的真实现象及其引入的人为偏差，并展示这些偏差如何影响目标准确率；第三，分析数据相关漏洞及其对应防御对收敛的影响，通过对比干净与对抗条件下的性能，明确收敛性与鲁棒性之间的权衡关系。据我们所知，这是首个全面理解联邦学习中数据相关挑战的综述。针对每类问题提炼出清晰建议，为实践者设计具备可预测收敛与稳定性的系统提供行动指南。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task model collaboratively without exposing their local data. While being a key component in any learning system, data is also a primary source of vulnerabilities and challenges, and a major determinant of a stable and well-converged training. Existing FL reviews describe general foundations, security practices, opportunities, challenges, and applications, without delving into diverse aspects of data and considering problems from the data perspective. They rarely provide a data-lens synthesis that links concrete data properties, split protocols, and defenses to convergence speed and stability. This survey fills that gap with three advances. First, we analyze non-IID into measurable traits and rank their influence on convergence as strong, medium, or light, explaining the mechanisms behind each and reconciling evidence across images, texts, and graphs. Second, we connect experimental splitting practices to the real phenomena they emulate, expose the artifacts they introduce, and show how those artifacts affect target accuracy. Third, we analyze how data-related vulnerabilities and their proposed defenses affect convergence, reporting performance under clean and adversarial conditions to make the convergence-robustness trade-off explicit. To our knowledge, this is the first survey to provide a complete understanding of data-related challenges that govern FL. With clear takeaways distilled for each concern, our work serves as actionable guidance, helping practitioners design their system with predictable convergence and stability.
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