Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than those trained in the standard centralized learning mode, especially when the training data are not independent and identically distributed (Non-IID) on the local devices. In this survey, we pro-vide a detailed analysis of the influence of Non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning. In addition, cur-rent research work on handling challenges of Non-IID data in federated learning are reviewed, and both advantages and disadvantages of these approaches are discussed. Finally, we suggest several future research directions before concluding the paper.

中文翻译：

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非 IID 数据的联合学习：一项调查

联邦学习是一种新兴的分布式机器学习框架，用于隐私保护。然而，联邦学习训练的模型通常比标准集中学习模式训练的模型性能更差，尤其是当训练数据在本地设备上不是独立同分布（非 IID）时。在本次调查中，我们详细分析了非 IID 数据对横向和纵向联邦学习中的参数和非参数机器学习模型的影响。此外，回顾了当前处理联邦学习中非 IID 数据挑战的研究工作，并讨论了这些方法的优缺点。最后，在结束本文之前，我们提出了几个未来的研究方向。