SEATTLE, October 1, 2025 - The Cancer AI Alliance (CAIA) - a research collaboration of top cancer centers - today announced the first scalable platform using federated learning for cancer research. CAIA’s work with Slalom was recently featured in the TIME100 AI 2025 list.

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), Slalom, Google, Microsoft and NVIDIA.

"At Slalom, we see the future of health as being the convergence of technology, data, and strategy to build better tomorrows for all," said Brenda Young, managing director at Slalom. "CAIA demonstrates how collaboration between leading cancer centers and technology partners can unlock new insights and reshape the use of Data & AI to diagnose, treat, and prevent cancer. We look forward to our continued partnership as we pave the way for transformative breakthroughs in health."

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.

While federated learning - a machine learning method that preserves anonymity of individual data - has been gaining steam for nearly 10 years, adapting the technology for multi-institution use in cancer research has proved elusive due to significant technological, regulatory, patient privacy and data harmonization challenges, as well as the coordination effort necessary to bring together organizations of this scale and complexity.

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.

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.

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 projects use the federated learning platform and structured, de-identified data housed securely by participating cancer centers, which collectively provides a diverse and representative foundation of over 1 million patients for modeling and analysis.

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.

For more information, visit https://www.canceralliance.ai/blog/caia-federated-learning-cancer-ai