The Cancer AI Alliance (CAIA) is a research collaboration of top cancer centers that recently announced the first scalable platform using federated learning for cancer research. The platform serves as the technological foundation of CAIA's aim to save more lives by enabling researchers and clinicians to train AI models that learn from participating cancer centers’ millions of clinical data points while maintaining data security, privacy and adherence to regulatory and ethical standards.

CAIA is comprised of National Cancer Institute-designated cancer centers Dana-Farber Cancer Institute, Fred Hutch Cancer Center, Memorial Sloan Kettering Cancer Center, and The Sidney Kimmel Comprehensive Cancer Center and Whiting School of Engineering at Johns Hopkins with financial and technical support from technology industry leaders Amazon Web Services (AWS), Deloitte, Ai2 (Allen Institute for AI), Google, Microsoft, NVIDIA and Slalom.

CAIA is a distributed alliance of institutions and individuals. Development of CAIA’s platform was possible due to an intense focus on collaboration and a drive for collective action across the participating cancer centers and technology companies, resulting in a unified technical, legal and governance structure.

Federated learning is a machine learning method that preserves anonymity of individual data. The federated learning platform works like this: participating cancer centers implement federated learning technology at their institutions, each connecting to a centralized orchestration component of the platform. Using this architecture, AI models travel to each participating cancer center’s secure data to learn from it locally, generating a summary of its learnings without individual clinical data ever leaving institutional firewalls. The insights gained from training the model on each cancer center’s de-identified data are then aggregated centrally to strengthen the AI models to uncover patterns, maximizing the value of the collective knowledge base.

CAIA's federated learning platform works by having AI models travel to each participating cancer center’s secure data to learn from it locally, generating a summary of its learnings without individual clinical data ever leaving institutional firewalls. The insights gained from training the model on each cancer center’s de-identified data are then aggregated centrally.

 By learning from diverse and representative patient populations across the country, AI models can uncover trends and patterns that may be missed by a single institution. This approach aims to improve health outcomes by revealing insights for a broader group of people, including those with rare cancers, and could lead to better discoveries, treatments, and improved clinical decision support.

Eight unique projects are being launched by researchers across the participating cancer centers. These initial projects aim to tackle some of oncology's most persistent challenges, from predicting treatment response to identifying novel biomarkers and analyzing rare cancer trends. The platform's true power, however, lies in its potential to scale up. Over the next year, CAIA plans to enable dozens of research models and add more participants to the alliance.

Development of CAIA’s platform was possible due to an intense focus on collaboration and a drive for collective action across the participating cancer centers and technology companies, resulting in a unified technical, legal and governance structure. CAIA represents a strategic shift leveraging collective strength rather than isolation.

Over the next year, CAIA plans to enable dozens of research models and add more participants to the alliance. This could serve as a model for every cancer center in the country to join CAIA in this collaborative effort to unlock innovation in cancer care.

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