Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities. FL has seen successful deployment in production environments, and it has been adopted in services such as virtual keyboards, auto-completion, item recommendation, and several IoT applications. However, FL comes with the challenge of performing training over largely heterogeneous datasets, devices, and networks that are out of the control of the centralized FL server. Motivated by this inherent setting, we make a first step towards characterizing the impact of device and behavioral heterogeneity on the trained model. We conduct an extensive empirical study spanning close to 1.5K unique configurations on five popular FL benchmarks. Our analysis shows that these sources of heterogeneity have a major impact on both model performance and fairness, thus sheds light on the importance of considering heterogeneity in FL system design.

中文翻译：

---

联合学习中设备和行为异质性的影响

联合学习（FL）成为在非信任实体拥有的分布式私有数据集上进行协作学习的流行范例。FL已在生产环境中成功部署，并且已在虚拟键盘，自动完成，项目推荐和多种IoT应用程序等服务中采用。但是，FL面临的挑战是对集中式FL服务器无法控制的大部分异构数据集，设备和网络进行训练。受此固有设置的激励，我们迈出了表征设备和行为异质性对训练模型的影响的第一步。我们对五种流行的FL基准进行了广泛的实证研究，涵盖了接近1.5K的独特配置。