Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap). In this work, we propose a framework for disentangling these performance gaps. Using this framework, we observe and explain differences in behavior across natural and synthetic federated datasets, indicating that dataset synthesis strategy can be important for realistic simulations of generalization in federated learning. We propose a semantic synthesis strategy that enables realistic simulation without naturally-partitioned data. Informed by our findings, we call out community suggestions for future federated learning works.

本文提出了一种用于区分表现差距的框架，以解决联邦学习中未见客户数据（样本外差距）与未见客户分布（参与差距）之间的性能差距，同时提出了语义综合策略，以便实现对联邦学习中一般化的现实模拟。在自然和合成联邦数据集上对此进行了观察和解释，旨在为未来的联邦学习工作提出建议。

联邦学习中的泛化是什么意思？