  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.
