Human mobility, climate change and demographic trends increase the risk of pathogen spillover and expansion. Data that can inform our responses to outbreaks have increased in availability and volume, but access to highly confidential outbreak data and commercially sensitive contextual information remains difficult. Despite ongoing efforts to adopt global health data infrastructures and sharing protocols, there remain regulatory, logistical, human and computational barriers to data sharing. Federated approaches—in which data remain stored locally but analyses are performed across datasets from different sources—offer a potential way to address these challenges. While federated approaches have been used in some clinical and biomedical contexts, their adoption in infectious disease surveillance and modeling has been limited. Here, we discuss global approaches to infectious disease modeling and analysis, with a focus on federated methods. We outline how these can be used to address key epidemiological questions during outbreaks by enabling the secure use of multimodal data and integration with existing surveillance and modeling efforts. We summarize current methods for combining distributed and locally stored data and identify limitations, opportunities and organizational structures needed to achieve equitable global public health impacts.