Federated Learning is widely employed to tackle distributed sensitive data. Existing methods primarily focus on addressing in-federation data heterogeneity. However, we observed that they suffer from significant performance degradation when applied to unseen clients for out-of-federation (OOF) generalization. The recent attempts to address generalization to unseen clients generally struggle to scale up to large-scale distributed settings due to high communication or computation costs. Moreover, methods that scale well often demonstrate poor generalization capability. To achieve OOF-resiliency in a scalable manner, we propose Topology-aware Federated Learning (TFL) that leverages client topology - a graph representing client relationships - to effectively train robust models against OOF data. We formulate a novel optimization problem for TFL, consisting of two key modules: Client Topology Learning, which infers the client relationships in a privacy-preserving manner, and Learning on Client Topology, which leverages the learned topology to identify influential clients and harness this information into the FL optimization process to efficiently build robust models. Empirical evaluation on a variety of real-world datasets verifies TFL's superior OOF robustness and scalability.

翻译：联邦学习被广泛用于处理分布式敏感数据。现有方法主要关注解决联邦内部的数据异质性问题。然而，我们观察到这些方法在应用于未见客户端以实现跨联邦（OOF）泛化时，会出现显著的性能下降。近期针对未见客户端泛化的尝试，由于高昂的通信或计算成本，通常难以扩展到大规模分布式场景。此外，那些可扩展性良好的方法往往表现出较差的泛化能力。为了以可扩展的方式实现OOF鲁棒性，我们提出了拓扑感知联邦学习（TFL），该方法利用客户端拓扑——一种表示客户端关系的图结构——来有效训练针对OOF数据的鲁棒模型。我们为TFL构建了一个新颖的优化问题，包含两个关键模块：客户端拓扑学习，以隐私保护的方式推断客户端关系；以及基于客户端拓扑的学习，利用学习到的拓扑识别有影响力的客户端，并将此信息融入联邦学习优化过程，以高效构建鲁棒模型。在多种真实世界数据集上的实证评估验证了TFL卓越的OOF鲁棒性和可扩展性。