Since you are reading this, you have likely encountered the challenge of analyzing data across multiple datasets. Combining insights from distributed data resources increases statistical power and is essential for reproducibility and validation.
Analyzing multiple datasets is challenging mainly due to two factors: comparability and accessibility. Datasets must be comparable in terms of measured variables and processing pipelines, and access to sensitive or large datasets is often restricted.
These challenges have led to the development of federated data analysis and federated learning approaches, which allow collaborative analytics without sharing raw data.
What are federated data analysis and federated learning?
Federated data analysis refers to analytical methods applied across multiple, often geographically separated datasets. The data remain at their original locations, for example behind institutional firewalls, while only analysis parameters are exchanged.
Federated approaches can be applied to classical statistics as well as machine learning, the latter often referred to as federated learning.
What are the advantages of federated analysis?
The main advantage is the ability to analyze data that cannot be shared. Properly implemented federated systems minimize the risk of exposing sensitive information while enabling larger sample sizes and independent validation.
Federated analysis is particularly valuable for consortium projects, large-scale studies, and datasets that are too sensitive or too large to centralize.
What should you consider when implementing federated analysis?
Successful federated projects require strong communication and agreement on data standards, processing strategies, and analysis pipelines across participating institutions.
Computational complexity is another important factor. Not all algorithms are suitable for federated use, and some parameter exchanges may pose privacy risks if not carefully designed.
It is recommended to start with simpler methods or established federated tools and to test workflows using simulated data before working with sensitive datasets.
What you need to get started
- Identify federated analysis technologies that fit your analytical needs, such as DataSHIELD for R-based workflows.
- Maintain an overview of available data resources, variables, and processing steps across participating sites.
- Establish clear governance rules defining which analyses are permitted on the distributed data infrastructure.
Example of a federated analysis system
DataSHIELD is an open-source platform for federated statistical analysis using R, allowing secure computation across institutional boundaries.
Ready to analyze?
Federated analysis enables secure, collaborative analytics on sensitive data. With careful planning and the right tools, it opens new opportunities for large-scale, privacy-preserving research.